This paper reviews the advances, challenges, and approaches of artificial intelligence (AI) and machine learning (ML) in the banking sector. The use of these technologies is accelerating in various industries, including banking. However, the literature on banking is scattered, making a global understanding difficult. This study reviewed the main approaches in terms of applications and algorithmic models, as well as the benefits and challenges associated with their implementation in banking, in addition to a bibliometric analysis of variables related to the distribution of publications and the most productive countries, as well as an analysis of the co-occurrence and dynamics of keywords. Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework, forty articles were selected for review. The results indicate that these technologies are used in the banking sector for customer segmentation, credit risk analysis, recommendation, and fraud detection. It should be noted that credit analysis and fraud detection are the most implemented areas, using algorithms such as random forests (RF), decision trees (DT), support vector machines (SVM), and logistic regression (LR), among others. In addition, their use brings significant benefits for decision-making and optimizing banking operations. However, the handling of substantial amounts of data with these technologies poses ethical challenges.
Full Paper: Analytics: Key to go from generating big data to deriving busines...Piyush Malik
This document discusses how analytics can help organizations derive business value from big data. It describes how statistical analysis, machine learning, optimization and text mining can extract meaningful insights from social media, online commerce, telecommunications, smart utility meters, and improve security. While tools exist to analyze big data, challenges remain around data security, privacy, and developing skilled talent. The paper aims to illustrate how existing algorithms can generate value from different industry use cases.
the influence of machine language and data science in the emerging worldijtsrd
The study describes the machine learning language with respect to big data sciences. The process of machine learning has evolved to have grown significantly to progress in information science. This progress has led to conquer different domains and are capable of solving myriad problems and upgrading the applicative properties. Hence, the present study is drafted to highlight the importance of machine learning process and language. Anitha. S "The Influence of Machine Language and Data Science in the Emerging World" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31907.pdf Paper Url :https://www.ijtsrd.com/engineering/computer-engineering/31907/the-influence-of-machine-language-and-data-science-in-the-emerging-world/anitha-s
A survey of big data and machine learning IJECEIAES
This paper presents a detailed analysis of big data and machine learning (ML) in the electrical power and energy sector. Big data analytics for smart energy operations, applications, impact, measurement and control, and challenges are presented in this paper. Big data and machine learning approaches need to be applied after analyzing the power system problem carefully. Determining the match between the strengths of big data and machine learning for solving the power system problem is of utmost important. They can be of great help to plan and operate the traditional grid/smart grid (SG). The basics of big data and machine learning are described in detailed manner along with their applications in various fields such as electrical power and energy, health care and life sciences, government, telecommunications, web and digital media, retailers, finance, e-commerce and customer service, etc. Finally, the challenges and opportunities of big data and machine learning are presented in this paper.
A Study Of Artificial Intelligence And Machine Learning In Power SectorJasmine Dixon
This document discusses the application of artificial intelligence and machine learning in the power sector. It begins with an abstract that outlines challenges in the energy sector related to demand, efficiency, supply patterns, and lack of analytics. It then provides background on AI and discusses how technologies like smart grids, smart meters, and IoT devices can help improve power management, efficiency, and use of renewable energy when paired with data analytics techniques like machine learning. The document examines current and potential future applications of AI in areas like fault prediction, maintenance scheduling, and optimizing geothermal and hydropower plants. It concludes by stating that while advanced economies currently lead in AI applications for power, the technologies have potential to address energy access challenges in emerging markets as
A Comprehensive Overview of Advance Techniques, Applications and Challenges i...IRJTAE
— The field of data science uses scientific methods, algorithms, processes, and systems to extract
insights and knowledge from structured and unstructured data. It combines principles from mathematics,
statistics, computer science, and domain expertise to analyse, interpret, and present data in meaningful ways. Its
primary aim is to uncover patterns, trends, and correlations across various domains to aid in making informed
decisions, predictions, and optimizations. Data science encompasses data collection, cleaning, analysis,
interpretation, and communication of findings. Techniques such as machine learning, statistical analysis, data
mining, and data visualization are commonly employed to derive valuable insights and solve complex problems.
Data scientists use programming languages and tools to manage large volumes of data, transforming raw
information into actionable intelligence, driving innovation, and enabling evidence-based decision-making in
businesses, research, and various other applications. This review seeks to provide a valuable resource for
researchers, practitioners, and enthusiasts who wish to gain in-depth knowledge and understanding of data
science and its implications for the ever-evolving data-driven world.
La Banque centrale du Luxembourg (BCL) et la Commission de surveillance du secteur financier (CSSF) ont publié le 3 mai un rapport conjoint sur l'utilisation de l'intelligence artificielle par la place financière.
THE TRANSFORMATION RISK-BENEFIT MODEL OF ARTIFICIAL INTELLIGENCE:BALANCING RI...gerogepatton
This paper summarizes the most cogent advantages and risks associated with Artificial Intelligence from an
in-depth review of the literature. Then the authors synthesize the salient risk-related models currently being
used in AI, technology and business-related scenarios. Next, in view of an updated context of AI along with
theories and models reviewed and expanded constructs, the writers propose a new framework called “The
Transformation Risk-Benefit Model of Artificial Intelligence” to address the increasing fears and levels of
AIrisk. Using the model characteristics, the article emphasizes practical and innovative solutions where
benefitsoutweigh risks and three use cases in healthcare, climate change/environment and cyber security to
illustrate unique interplay of principles, dimensions and processes of this powerful AI transformational
model.
Full Paper: Analytics: Key to go from generating big data to deriving busines...Piyush Malik
This document discusses how analytics can help organizations derive business value from big data. It describes how statistical analysis, machine learning, optimization and text mining can extract meaningful insights from social media, online commerce, telecommunications, smart utility meters, and improve security. While tools exist to analyze big data, challenges remain around data security, privacy, and developing skilled talent. The paper aims to illustrate how existing algorithms can generate value from different industry use cases.
the influence of machine language and data science in the emerging worldijtsrd
The study describes the machine learning language with respect to big data sciences. The process of machine learning has evolved to have grown significantly to progress in information science. This progress has led to conquer different domains and are capable of solving myriad problems and upgrading the applicative properties. Hence, the present study is drafted to highlight the importance of machine learning process and language. Anitha. S "The Influence of Machine Language and Data Science in the Emerging World" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31907.pdf Paper Url :https://www.ijtsrd.com/engineering/computer-engineering/31907/the-influence-of-machine-language-and-data-science-in-the-emerging-world/anitha-s
A survey of big data and machine learning IJECEIAES
This paper presents a detailed analysis of big data and machine learning (ML) in the electrical power and energy sector. Big data analytics for smart energy operations, applications, impact, measurement and control, and challenges are presented in this paper. Big data and machine learning approaches need to be applied after analyzing the power system problem carefully. Determining the match between the strengths of big data and machine learning for solving the power system problem is of utmost important. They can be of great help to plan and operate the traditional grid/smart grid (SG). The basics of big data and machine learning are described in detailed manner along with their applications in various fields such as electrical power and energy, health care and life sciences, government, telecommunications, web and digital media, retailers, finance, e-commerce and customer service, etc. Finally, the challenges and opportunities of big data and machine learning are presented in this paper.
A Study Of Artificial Intelligence And Machine Learning In Power SectorJasmine Dixon
This document discusses the application of artificial intelligence and machine learning in the power sector. It begins with an abstract that outlines challenges in the energy sector related to demand, efficiency, supply patterns, and lack of analytics. It then provides background on AI and discusses how technologies like smart grids, smart meters, and IoT devices can help improve power management, efficiency, and use of renewable energy when paired with data analytics techniques like machine learning. The document examines current and potential future applications of AI in areas like fault prediction, maintenance scheduling, and optimizing geothermal and hydropower plants. It concludes by stating that while advanced economies currently lead in AI applications for power, the technologies have potential to address energy access challenges in emerging markets as
A Comprehensive Overview of Advance Techniques, Applications and Challenges i...IRJTAE
— The field of data science uses scientific methods, algorithms, processes, and systems to extract
insights and knowledge from structured and unstructured data. It combines principles from mathematics,
statistics, computer science, and domain expertise to analyse, interpret, and present data in meaningful ways. Its
primary aim is to uncover patterns, trends, and correlations across various domains to aid in making informed
decisions, predictions, and optimizations. Data science encompasses data collection, cleaning, analysis,
interpretation, and communication of findings. Techniques such as machine learning, statistical analysis, data
mining, and data visualization are commonly employed to derive valuable insights and solve complex problems.
Data scientists use programming languages and tools to manage large volumes of data, transforming raw
information into actionable intelligence, driving innovation, and enabling evidence-based decision-making in
businesses, research, and various other applications. This review seeks to provide a valuable resource for
researchers, practitioners, and enthusiasts who wish to gain in-depth knowledge and understanding of data
science and its implications for the ever-evolving data-driven world.
La Banque centrale du Luxembourg (BCL) et la Commission de surveillance du secteur financier (CSSF) ont publié le 3 mai un rapport conjoint sur l'utilisation de l'intelligence artificielle par la place financière.
THE TRANSFORMATION RISK-BENEFIT MODEL OF ARTIFICIAL INTELLIGENCE:BALANCING RI...gerogepatton
This paper summarizes the most cogent advantages and risks associated with Artificial Intelligence from an
in-depth review of the literature. Then the authors synthesize the salient risk-related models currently being
used in AI, technology and business-related scenarios. Next, in view of an updated context of AI along with
theories and models reviewed and expanded constructs, the writers propose a new framework called “The
Transformation Risk-Benefit Model of Artificial Intelligence” to address the increasing fears and levels of
AIrisk. Using the model characteristics, the article emphasizes practical and innovative solutions where
benefitsoutweigh risks and three use cases in healthcare, climate change/environment and cyber security to
illustrate unique interplay of principles, dimensions and processes of this powerful AI transformational
model.
THE TRANSFORMATION RISK-BENEFIT MODEL OF ARTIFICIAL INTELLIGENCE:BALANCING RI...ijaia
This paper summarizes the most cogent advantages and risks associated with Artificial Intelligence from an
in-depth review of the literature. Then the authors synthesize the salient risk-related models currently being
used in AI, technology and business-related scenarios. Next, in view of an updated context of AI along with
theories and models reviewed and expanded constructs, the writers propose a new framework called “The
Transformation Risk-Benefit Model of Artificial Intelligence” to address the increasing fears and levels of
AIrisk. Using the model characteristics, the article emphasizes practical and innovative solutions where
benefitsoutweigh risks and three use cases in healthcare, climate change/environment and cyber security to
illustrate unique interplay of principles, dimensions and processes of this powerful AI transformational
model.
THE TRANSFORMATION RISK-BENEFIT MODEL OF ARTIFICIAL INTELLIGENCE:BALANCING RI...ijaia
This paper summarizes the most cogent advantages and risks associated with Artificial Intelligence from an
in-depth review of the literature. Then the authors synthesize the salient risk-related models currently being
used in AI, technology and business-related scenarios. Next, in view of an updated context of AI along with
theories and models reviewed and expanded constructs, the writers propose a new framework called “The
Transformation Risk-Benefit Model of Artificial Intelligence” to address the increasing fears and levels of
AIrisk. Using the model characteristics, the article emphasizes practical and innovative solutions where
benefitsoutweigh risks and three use cases in healthcare, climate change/environment and cyber security to
illustrate unique interplay of principles, dimensions and processes of this powerful AI transformational
model.
Machine learning applications used in accounting and auditsvivatechijri
AI is a territory of software engineering that gains from a lot of information, recognizes examples, and makes expectations about future occasions. In the accounting and auditing professions, Machine Learning has been progressively utilized over the most recent couple of years. Thusly, this investigation means to Survey the current Machine Learning applications in accounting and auditing with a focus on Big Four Organizations. In this study, the AI devices and stages created by Big Four organizations are analyzed by directing a content investigation. It has been distinguished that Big Four organizations built up a few Machine learning devices that are utilized for predictable audits coordination and the management, completely automated audits. Accounting processes such as accounts receivable and accounts payable management, preparation of expense reports, and risk assessment can easily be automated by AI. For instance, machine learning algorithms can match an invoice received, decide the right business ledger for acknowledgment, and place it in a payment pool where a human specialist can inspect and submit the payment request to the payment queue.
This document discusses the role of artificial intelligence in business risk management and cost modeling. It begins by outlining the growth of AI and its applications in various domains including the financial sector. It then discusses how AI aids in risk management and cost modeling by helping to understand and measure the variables involved. The document also describes three methods for cost estimation and modeling using AI techniques: analog, analytical, and parametric. It argues that AI can provide strategies and applications to enhance financial risk management and cost modeling.
A Review of Big Data Analytics in Sector of Higher EducationIJERA Editor
This paper is about the use of big data analytics in higher education. In this paper, we see what the big data is and where does it come from. We will also try to find why the big data analytics has become a buzzword in almost every sector today through our literature review on the big data analytics and its applications in higher education sector. Then we see what the big educational data is, how it is generated and analyzed. We found that the two most important types of analytics are- Learning and academic analytics which will be discussed. Several papers describe the benefits of implementation of analytics in the education sector and the opportunities provided which will be discussed in this paper. We also found that the basic characteristics such as size, speed, variety and some other factors are responsible for some issues and challenges to the use of analytics in this sector. We will discuss those issues and challenges and discuss some proposed solutions to address them.
THE FRAMEWORK OF ARTIFICIAL INTELLIGENCE (FAI): DRIVING TRIGGERS, STATE OF TH...ijaia
The authors present a new Framework of Artificial Intelligence which analyzes the key elements of
transformational AI in industry. The State of the Art of Artificial Intelligence is gleaned from an
examination of what has been done in the past, presently in the last decade and what is predicted for future
decades. The paper will highlight the biggest changes in AI, important influencers to adoption/diffusion
and give examples of how these technologies have and will be applied in three key industrial sectors,
including agriculture, education and healthcare. Next the research examines seven driving triggers of cost,
speed, accuracy, diversity/inclusion, interdisciplinary research/collaboration and ethics/trustworthiness
that are accelerating AI development and concludes with a discussion of what are the critical success
factors for industry to be transformational in AI.
A Study on Role of Technology in Banking Sectorijtsrd
The purpose of this study is to examine the relationship between new technology implementation in banking sector and customers How they are aware about the technologies and how they are using it Data for this study was collected from the customers of various Banking Sectors under the Reserve Bank of India A simple percentage analysis and pie chart will be done According to questioners 30 samples are collected and interpretations are given Findings suggest that most of the customers of bank using ATM facility So the banks need to give awareness about the E-banking services Lastly, the paper is of few papers that focus on technology development in banking industry Aswin Raj. T | Mr. Bala Nageshwara Rao "A Study on Role of Technology in Banking Sector" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-6 , October 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18357.pdf
Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of
such advanced technology, there will be always a question regarding its impact on our social life,
environment and economy thus impacting all efforts exerted towards sustainable development. In the
information era, enormous amounts of data have become available on hand to decision makers. Big data
refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to
handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be
studied and provided in order to handle and extract value and knowledge from these datasets for different
industries and business operations. Numerous use cases have shown that AI can ensure an effective supply
of information to citizens, users and customers in times of crisis. This paper aims to analyse some of the
different methods and scenario which can be applied to AI and big data, as well as the opportunities
provided by the application in various business operations and crisis management domains.
Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of
such advanced technology, there will be always a question regarding its impact on our social life,
environment and economy thus impacting all efforts exerted towards sustainable development. In the
information era, enormous amounts of data have become available on hand to decision makers. Big data
refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to
handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be
studied and provided in order to handle and extract value and knowledge from these datasets for different
industries and business operations. Numerous use cases have shown that AI can ensure an effective supply
of information to citizens, users and customers in times of crisis. This paper aims to analyse some of the
different methods and scenario which can be applied to AI and big data, as well as the opportunities
provided by the application in various business operations and crisis management domains.
Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of such advanced technology, there will be always a question regarding its impact on our social life, environment and economy thus impacting all efforts exerted towards sustainable development. In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets for different industries and business operations. Numerous use cases have shown that AI can ensure an effective supply of information to citizens, users and customers in times of crisis. This paper aims to analyse some of the different methods and scenario which can be applied to AI and big data, as well as the opportunities provided by the application in various business operations and crisis management domains.
This document discusses the role of artificial intelligence in shaping consumer demand in e-commerce. It explores how AI techniques like sentiment analysis, opinion mining, deep learning and machine learning are used to understand customer behavior and influence marketing strategies. The document also addresses issues regarding the lack of explainability in AI systems and proposes using Explainable AI models to make systems more interpretable and address ethical concerns.
A forecasting of stock trading price using time series information based on b...IJECEIAES
Big data is a large set of structured or unstructured data that can collect, store, manage, and analyze data with existing database management tools. And it means the technique of extracting value from these data and interpreting the results. Big data has three characteristics: The size of existing data and other data (volume), the speed of data generation (velocity), and the variety of information forms (variety). The time series data are obtained by collecting and recording the data generated in accordance with the flow of time. If the analysis of these time series data, found the characteristics of the data implies that feature helps to understand and analyze time series data. The concept of distance is the simplest and the most obvious in dealing with the similarities between objects. The commonly used and widely known method for measuring distance is the Euclidean distance. This study is the result of analyzing the similarity of stock price flow using 793,800 closing prices of 1,323 companies in Korea. Visual studio and Excel presented calculate the Euclidean distance using an analysis tool. We selected “000100” as a target domestic company and prepared for big data analysis. As a result of the analysis, the shortest Euclidean distance is the code “143860” company, and the calculated value is “11.147”. Therefore, based on the results of the analysis, the limitations of the study and theoretical implications are suggested.
This document presents a system for predicting corporate bankruptcy using textual disclosures from SEC filings. It discusses how previous studies have used financial ratios and market data to predict bankruptcy, but that textual disclosures also provide important unstructured qualitative information. The proposed system uses natural language processing and machine learning algorithms to extract features from 10-K and 10-Q filings and predict bankruptcy with high accuracy, even before the final bankruptcy occurs. It aims to improve on previous bankruptcy prediction methods by incorporating both financial and textual data sources.
A COMPREHENSIVE STUDY ON POTENTIAL RESEARCH OPPORTUNITIES OF BIG DATA ANALYTI...ijcseit
Companies, organizations and policy makers shake out with flood flowing volume of transactional data,
accumulating trillions of bytes of information about their customers, suppliers and operations. The advanced networked sensors are being implanted in devices such as mobile phones, smart energy meters,automobiles and industrial machines that sense, generate and transfer data to multiple storage devices. In fact, as they go about their business and interact with individuals, they are producing an incredible amount of fatigue digital data. Social media sites, smart phones, and other customer devices have allowed billions
of individuals around the world to contribute to the amount of data available. In addition, the extremely
increasing size of multimedia data has also take part a key role in the rapid growth of data. The technology
of high-definition video creates more than 2,000 times as many bytes as necessary to store as normal text
data. Moreover, in a digitized world, consumers are leaving enormous amount of data about their day-today
communicating, browsing, buying, sharing, searching and so on. As a result, it evolved as a big data and in turn has motivated the advances in big data analytics paradigms, endorsed as a basic motivation factor for the present researchers.
A COMPREHENSIVE STUDY ON POTENTIAL RESEARCH OPPORTUNITIES OF BIG DATA ANALYTI...ijcseit
Companies, organizations and policy makers shake out with flood flowing volume of transactional data, accumulating trillions of bytes of information about their customers, suppliers and operations. The advanced networked sensors are being implanted in devices such as mobile phones, smart energy meters, automobiles and industrial machines that sense, generate and transfer data to multiple storage devices. In fact, as they go about their business and interact with individuals, they are producing an incredible amount of fatigue digital data. Social media sites, smart phones, and other customer devices have allowed billions of individuals around the world to contribute to the amount of data available. In addition, the extremely increasing size of multimedia data has also take part a key role in the rapid growth of data. The technology of high-definition video creates more than 2,000 times as many bytes as necessary to store as normal text data. Moreover, in a digitized world, consumers are leaving enormous amount of data about their day-today communicating, browsing, buying, sharing, searching and so on. As a result, it evolved as a big data and in turn has motivated the advances in big data analytics paradigms, endorsed as a basic motivation factor for the present researchers.
The authors in the present paper conduct a comprehensive study to explore the impact of big data analytics in key domains namely, Health Care (HC), Retail Industry (RI), Public Governance (PG), Pubic Security & Safety (PSS) and Personal Location Tracking (PLT). Initially, the study looks at the insights of data sources along with their characteristics in each domain. Later, it presents the highly productive and competitive big data applications with innovative big data technologies. Subsequently, the study showcases the impact of big data on each domain to capture value addition in its services. Finally, the study put forwards many more research opportunities as all these domains differ in their complexity and development in the usage of big data analytics
A COMPREHENSIVE STUDY ON POTENTIAL RESEARCH OPPORTUNITIES OF BIG DATA ANALYTI...ijcseit
Companies, organizations and policy makers shake out with flood flowing volume of transactional data,
accumulating trillions of bytes of information about their customers, suppliers and operations. The
advanced networked sensors are being implanted in devices such as mobile phones, smart energy meters,
automobiles and industrial machines that sense, generate and transfer data to multiple storage devices. In
fact, as they go about their business and interact with individuals, they are producing an incredible amount
of fatigue digital data. Social media sites, smart phones, and other customer devices have allowed billions
of individuals around the world to contribute to the amount of data available. In addition, the extremely
increasing size of multimedia data has also take part a key role in the rapid growth of data. The technology
of high-definition video creates more than 2,000 times as many bytes as necessary to store as normal text
data. Moreover, in a digitized world, consumers are leaving enormous amount of data about their day-today communicating, browsing, buying, sharing, searching and so on. As a result, it evolved as a big data
and in turn has motivated the advances in big data analytics paradigms, endorsed as a basic motivation
factor for the present researchers.
The authors in the present paper conduct a comprehensive study to explore the impact of big data analytics
in key domains namely, Health Care (HC), Retail Industry (RI), Public Governance (PG), Pubic Security &
Safety (PSS) and Personal Location Tracking (PLT). Initially, the study looks at the insights of data sources
along with their characteristics in each domain. Later, it presents the highly productive and competitive big
data applications with innovative big data technologies. Subsequently, the study showcases the impact of
big data on each domain to capture value addition in its services. Finally, the study put forwards many
more research opportunities as all these domains differ in their complexity and development in the usage of
big data analytics.
This document is a synopsis submitted by Santosh Malaiah Ralabandi to RTM Nagpur University in partial fulfillment of an MBA degree. It examines the impact of technology on financial services. Technology plays a major role in modern industries and financial institutions must invest in information technology to remain competitive. The financial industry relies heavily on advanced technologies to process high volumes of transactions that could not be handled manually. The synopsis will focus on how technology influences competition and service delivery in the financial sector.
Presentation about the state of AI, policy-relevant AI research and evidence gaps that can be addressed with new data, methods and modelling approaches.
A Novel Approach for Forecasting Disease Using Machine LearningIRJET Journal
This document discusses using machine learning models to predict diseases. It analyzes several supervised machine learning algorithms, including Naive Bayes, Decision Trees, K-Nearest Neighbors, Logistic Regression, and Convolutional Neural Networks. The key findings are:
1) K-Nearest Neighbors performed best at predicting kidney disease, Parkinson's disease, and heart disease based on the analyses.
2) Logistic Regression and Convolutional Neural Networks predicted breast cancer and common diseases accurately, respectively.
3) Supervised machine learning algorithms show potential for early disease detection when applied to electronic health data, which can help clinicians and improve patient outcomes.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
More Related Content
Similar to Financial revolution: a systemic analysis of artificial intelligence and machine learning in the banking sector
THE TRANSFORMATION RISK-BENEFIT MODEL OF ARTIFICIAL INTELLIGENCE:BALANCING RI...ijaia
This paper summarizes the most cogent advantages and risks associated with Artificial Intelligence from an
in-depth review of the literature. Then the authors synthesize the salient risk-related models currently being
used in AI, technology and business-related scenarios. Next, in view of an updated context of AI along with
theories and models reviewed and expanded constructs, the writers propose a new framework called “The
Transformation Risk-Benefit Model of Artificial Intelligence” to address the increasing fears and levels of
AIrisk. Using the model characteristics, the article emphasizes practical and innovative solutions where
benefitsoutweigh risks and three use cases in healthcare, climate change/environment and cyber security to
illustrate unique interplay of principles, dimensions and processes of this powerful AI transformational
model.
THE TRANSFORMATION RISK-BENEFIT MODEL OF ARTIFICIAL INTELLIGENCE:BALANCING RI...ijaia
This paper summarizes the most cogent advantages and risks associated with Artificial Intelligence from an
in-depth review of the literature. Then the authors synthesize the salient risk-related models currently being
used in AI, technology and business-related scenarios. Next, in view of an updated context of AI along with
theories and models reviewed and expanded constructs, the writers propose a new framework called “The
Transformation Risk-Benefit Model of Artificial Intelligence” to address the increasing fears and levels of
AIrisk. Using the model characteristics, the article emphasizes practical and innovative solutions where
benefitsoutweigh risks and three use cases in healthcare, climate change/environment and cyber security to
illustrate unique interplay of principles, dimensions and processes of this powerful AI transformational
model.
Machine learning applications used in accounting and auditsvivatechijri
AI is a territory of software engineering that gains from a lot of information, recognizes examples, and makes expectations about future occasions. In the accounting and auditing professions, Machine Learning has been progressively utilized over the most recent couple of years. Thusly, this investigation means to Survey the current Machine Learning applications in accounting and auditing with a focus on Big Four Organizations. In this study, the AI devices and stages created by Big Four organizations are analyzed by directing a content investigation. It has been distinguished that Big Four organizations built up a few Machine learning devices that are utilized for predictable audits coordination and the management, completely automated audits. Accounting processes such as accounts receivable and accounts payable management, preparation of expense reports, and risk assessment can easily be automated by AI. For instance, machine learning algorithms can match an invoice received, decide the right business ledger for acknowledgment, and place it in a payment pool where a human specialist can inspect and submit the payment request to the payment queue.
This document discusses the role of artificial intelligence in business risk management and cost modeling. It begins by outlining the growth of AI and its applications in various domains including the financial sector. It then discusses how AI aids in risk management and cost modeling by helping to understand and measure the variables involved. The document also describes three methods for cost estimation and modeling using AI techniques: analog, analytical, and parametric. It argues that AI can provide strategies and applications to enhance financial risk management and cost modeling.
A Review of Big Data Analytics in Sector of Higher EducationIJERA Editor
This paper is about the use of big data analytics in higher education. In this paper, we see what the big data is and where does it come from. We will also try to find why the big data analytics has become a buzzword in almost every sector today through our literature review on the big data analytics and its applications in higher education sector. Then we see what the big educational data is, how it is generated and analyzed. We found that the two most important types of analytics are- Learning and academic analytics which will be discussed. Several papers describe the benefits of implementation of analytics in the education sector and the opportunities provided which will be discussed in this paper. We also found that the basic characteristics such as size, speed, variety and some other factors are responsible for some issues and challenges to the use of analytics in this sector. We will discuss those issues and challenges and discuss some proposed solutions to address them.
THE FRAMEWORK OF ARTIFICIAL INTELLIGENCE (FAI): DRIVING TRIGGERS, STATE OF TH...ijaia
The authors present a new Framework of Artificial Intelligence which analyzes the key elements of
transformational AI in industry. The State of the Art of Artificial Intelligence is gleaned from an
examination of what has been done in the past, presently in the last decade and what is predicted for future
decades. The paper will highlight the biggest changes in AI, important influencers to adoption/diffusion
and give examples of how these technologies have and will be applied in three key industrial sectors,
including agriculture, education and healthcare. Next the research examines seven driving triggers of cost,
speed, accuracy, diversity/inclusion, interdisciplinary research/collaboration and ethics/trustworthiness
that are accelerating AI development and concludes with a discussion of what are the critical success
factors for industry to be transformational in AI.
A Study on Role of Technology in Banking Sectorijtsrd
The purpose of this study is to examine the relationship between new technology implementation in banking sector and customers How they are aware about the technologies and how they are using it Data for this study was collected from the customers of various Banking Sectors under the Reserve Bank of India A simple percentage analysis and pie chart will be done According to questioners 30 samples are collected and interpretations are given Findings suggest that most of the customers of bank using ATM facility So the banks need to give awareness about the E-banking services Lastly, the paper is of few papers that focus on technology development in banking industry Aswin Raj. T | Mr. Bala Nageshwara Rao "A Study on Role of Technology in Banking Sector" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-6 , October 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18357.pdf
Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of
such advanced technology, there will be always a question regarding its impact on our social life,
environment and economy thus impacting all efforts exerted towards sustainable development. In the
information era, enormous amounts of data have become available on hand to decision makers. Big data
refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to
handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be
studied and provided in order to handle and extract value and knowledge from these datasets for different
industries and business operations. Numerous use cases have shown that AI can ensure an effective supply
of information to citizens, users and customers in times of crisis. This paper aims to analyse some of the
different methods and scenario which can be applied to AI and big data, as well as the opportunities
provided by the application in various business operations and crisis management domains.
Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of
such advanced technology, there will be always a question regarding its impact on our social life,
environment and economy thus impacting all efforts exerted towards sustainable development. In the
information era, enormous amounts of data have become available on hand to decision makers. Big data
refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to
handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be
studied and provided in order to handle and extract value and knowledge from these datasets for different
industries and business operations. Numerous use cases have shown that AI can ensure an effective supply
of information to citizens, users and customers in times of crisis. This paper aims to analyse some of the
different methods and scenario which can be applied to AI and big data, as well as the opportunities
provided by the application in various business operations and crisis management domains.
Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of such advanced technology, there will be always a question regarding its impact on our social life, environment and economy thus impacting all efforts exerted towards sustainable development. In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets for different industries and business operations. Numerous use cases have shown that AI can ensure an effective supply of information to citizens, users and customers in times of crisis. This paper aims to analyse some of the different methods and scenario which can be applied to AI and big data, as well as the opportunities provided by the application in various business operations and crisis management domains.
This document discusses the role of artificial intelligence in shaping consumer demand in e-commerce. It explores how AI techniques like sentiment analysis, opinion mining, deep learning and machine learning are used to understand customer behavior and influence marketing strategies. The document also addresses issues regarding the lack of explainability in AI systems and proposes using Explainable AI models to make systems more interpretable and address ethical concerns.
A forecasting of stock trading price using time series information based on b...IJECEIAES
Big data is a large set of structured or unstructured data that can collect, store, manage, and analyze data with existing database management tools. And it means the technique of extracting value from these data and interpreting the results. Big data has three characteristics: The size of existing data and other data (volume), the speed of data generation (velocity), and the variety of information forms (variety). The time series data are obtained by collecting and recording the data generated in accordance with the flow of time. If the analysis of these time series data, found the characteristics of the data implies that feature helps to understand and analyze time series data. The concept of distance is the simplest and the most obvious in dealing with the similarities between objects. The commonly used and widely known method for measuring distance is the Euclidean distance. This study is the result of analyzing the similarity of stock price flow using 793,800 closing prices of 1,323 companies in Korea. Visual studio and Excel presented calculate the Euclidean distance using an analysis tool. We selected “000100” as a target domestic company and prepared for big data analysis. As a result of the analysis, the shortest Euclidean distance is the code “143860” company, and the calculated value is “11.147”. Therefore, based on the results of the analysis, the limitations of the study and theoretical implications are suggested.
This document presents a system for predicting corporate bankruptcy using textual disclosures from SEC filings. It discusses how previous studies have used financial ratios and market data to predict bankruptcy, but that textual disclosures also provide important unstructured qualitative information. The proposed system uses natural language processing and machine learning algorithms to extract features from 10-K and 10-Q filings and predict bankruptcy with high accuracy, even before the final bankruptcy occurs. It aims to improve on previous bankruptcy prediction methods by incorporating both financial and textual data sources.
A COMPREHENSIVE STUDY ON POTENTIAL RESEARCH OPPORTUNITIES OF BIG DATA ANALYTI...ijcseit
Companies, organizations and policy makers shake out with flood flowing volume of transactional data,
accumulating trillions of bytes of information about their customers, suppliers and operations. The advanced networked sensors are being implanted in devices such as mobile phones, smart energy meters,automobiles and industrial machines that sense, generate and transfer data to multiple storage devices. In fact, as they go about their business and interact with individuals, they are producing an incredible amount of fatigue digital data. Social media sites, smart phones, and other customer devices have allowed billions
of individuals around the world to contribute to the amount of data available. In addition, the extremely
increasing size of multimedia data has also take part a key role in the rapid growth of data. The technology
of high-definition video creates more than 2,000 times as many bytes as necessary to store as normal text
data. Moreover, in a digitized world, consumers are leaving enormous amount of data about their day-today
communicating, browsing, buying, sharing, searching and so on. As a result, it evolved as a big data and in turn has motivated the advances in big data analytics paradigms, endorsed as a basic motivation factor for the present researchers.
A COMPREHENSIVE STUDY ON POTENTIAL RESEARCH OPPORTUNITIES OF BIG DATA ANALYTI...ijcseit
Companies, organizations and policy makers shake out with flood flowing volume of transactional data, accumulating trillions of bytes of information about their customers, suppliers and operations. The advanced networked sensors are being implanted in devices such as mobile phones, smart energy meters, automobiles and industrial machines that sense, generate and transfer data to multiple storage devices. In fact, as they go about their business and interact with individuals, they are producing an incredible amount of fatigue digital data. Social media sites, smart phones, and other customer devices have allowed billions of individuals around the world to contribute to the amount of data available. In addition, the extremely increasing size of multimedia data has also take part a key role in the rapid growth of data. The technology of high-definition video creates more than 2,000 times as many bytes as necessary to store as normal text data. Moreover, in a digitized world, consumers are leaving enormous amount of data about their day-today communicating, browsing, buying, sharing, searching and so on. As a result, it evolved as a big data and in turn has motivated the advances in big data analytics paradigms, endorsed as a basic motivation factor for the present researchers.
The authors in the present paper conduct a comprehensive study to explore the impact of big data analytics in key domains namely, Health Care (HC), Retail Industry (RI), Public Governance (PG), Pubic Security & Safety (PSS) and Personal Location Tracking (PLT). Initially, the study looks at the insights of data sources along with their characteristics in each domain. Later, it presents the highly productive and competitive big data applications with innovative big data technologies. Subsequently, the study showcases the impact of big data on each domain to capture value addition in its services. Finally, the study put forwards many more research opportunities as all these domains differ in their complexity and development in the usage of big data analytics
A COMPREHENSIVE STUDY ON POTENTIAL RESEARCH OPPORTUNITIES OF BIG DATA ANALYTI...ijcseit
Companies, organizations and policy makers shake out with flood flowing volume of transactional data,
accumulating trillions of bytes of information about their customers, suppliers and operations. The
advanced networked sensors are being implanted in devices such as mobile phones, smart energy meters,
automobiles and industrial machines that sense, generate and transfer data to multiple storage devices. In
fact, as they go about their business and interact with individuals, they are producing an incredible amount
of fatigue digital data. Social media sites, smart phones, and other customer devices have allowed billions
of individuals around the world to contribute to the amount of data available. In addition, the extremely
increasing size of multimedia data has also take part a key role in the rapid growth of data. The technology
of high-definition video creates more than 2,000 times as many bytes as necessary to store as normal text
data. Moreover, in a digitized world, consumers are leaving enormous amount of data about their day-today communicating, browsing, buying, sharing, searching and so on. As a result, it evolved as a big data
and in turn has motivated the advances in big data analytics paradigms, endorsed as a basic motivation
factor for the present researchers.
The authors in the present paper conduct a comprehensive study to explore the impact of big data analytics
in key domains namely, Health Care (HC), Retail Industry (RI), Public Governance (PG), Pubic Security &
Safety (PSS) and Personal Location Tracking (PLT). Initially, the study looks at the insights of data sources
along with their characteristics in each domain. Later, it presents the highly productive and competitive big
data applications with innovative big data technologies. Subsequently, the study showcases the impact of
big data on each domain to capture value addition in its services. Finally, the study put forwards many
more research opportunities as all these domains differ in their complexity and development in the usage of
big data analytics.
This document is a synopsis submitted by Santosh Malaiah Ralabandi to RTM Nagpur University in partial fulfillment of an MBA degree. It examines the impact of technology on financial services. Technology plays a major role in modern industries and financial institutions must invest in information technology to remain competitive. The financial industry relies heavily on advanced technologies to process high volumes of transactions that could not be handled manually. The synopsis will focus on how technology influences competition and service delivery in the financial sector.
Presentation about the state of AI, policy-relevant AI research and evidence gaps that can be addressed with new data, methods and modelling approaches.
A Novel Approach for Forecasting Disease Using Machine LearningIRJET Journal
This document discusses using machine learning models to predict diseases. It analyzes several supervised machine learning algorithms, including Naive Bayes, Decision Trees, K-Nearest Neighbors, Logistic Regression, and Convolutional Neural Networks. The key findings are:
1) K-Nearest Neighbors performed best at predicting kidney disease, Parkinson's disease, and heart disease based on the analyses.
2) Logistic Regression and Convolutional Neural Networks predicted breast cancer and common diseases accurately, respectively.
3) Supervised machine learning algorithms show potential for early disease detection when applied to electronic health data, which can help clinicians and improve patient outcomes.
Similar to Financial revolution: a systemic analysis of artificial intelligence and machine learning in the banking sector (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
The CBC machine is a common diagnostic tool used by doctors to measure a patient's red blood cell count, white blood cell count and platelet count. The machine uses a small sample of the patient's blood, which is then placed into special tubes and analyzed. The results of the analysis are then displayed on a screen for the doctor to review. The CBC machine is an important tool for diagnosing various conditions, such as anemia, infection and leukemia. It can also help to monitor a patient's response to treatment.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Financial revolution: a systemic analysis of artificial intelligence and machine learning in the banking sector
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 14, No. 1, February 2024, pp. 1079~1090
ISSN: 2088-8708, DOI: 10.11591/ijece.v14i1.pp1079-1090 1079
Journal homepage: http://ijece.iaescore.com
Financial revolution: a systemic analysis of artificial intelligence
and machine learning in the banking sector
Raúl Jáuregui-Velarde1
, Laberiano Andrade-Arenas2
, Pedro Molina-Velarde3
, Cesar Yactayo-Arias4
1
Facultad de Ingeniería y Negocios, Universidad Privada Norbert Wiener, Lima, Perú
2
Facultad de Ciencias e Ingeniería, Universidad de Ciencias y Humanidades, Lima, Perú
3
Facultad de Ingeniería, Universidad Tecnológica del Perú, Lima, Perú
4
Departamento de Estudios Generales, Universidad Continental, Lima, Perú
Article Info ABSTRACT
Article history:
Received Aug 25, 2023
Revised Oct 9, 2023
Accepted Oct 1, 2023
This paper reviews the advances, challenges, and approaches of artificial
intelligence (AI) and machine learning (ML) in the banking sector. The use
of these technologies is accelerating in various industries, including banking.
However, the literature on banking is scattered, making a global
understanding difficult. This study reviewed the main approaches in terms of
applications and algorithmic models, as well as the benefits and challenges
associated with their implementation in banking, in addition to a
bibliometric analysis of variables related to the distribution of publications
and the most productive countries, as well as an analysis of the
co-occurrence and dynamics of keywords. Following the preferred reporting
items for systematic reviews and meta-analyses (PRISMA) framework, forty
articles were selected for review. The results indicate that these technologies
are used in the banking sector for customer segmentation, credit risk
analysis, recommendation, and fraud detection. It should be noted that credit
analysis and fraud detection are the most implemented areas, using
algorithms such as random forests (RF), decision trees (DT), support vector
machines (SVM), and logistic regression (LR), among others. In addition,
their use brings significant benefits for decision-making and optimizing
banking operations. However, the handling of substantial amounts of data
with these technologies poses ethical challenges.
Keywords:
Artificial intelligence
Banking sector
Ethical challenges
Fraud detection
Machine learning
This is an open access article under the CC BY-SA license.
Corresponding Author:
Cesar Yactayo-Arias
Departamento de Estudios Generales, Universidad Continental
Avenida Alfredo Mendiola-5210, Lima, Perú
Email: cyactayo@continental.edu.pe
1. INTRODUCTION
In the banking sector, artificial intelligence (AI) and machine learning (ML) are driving big change.
AI can be described as a system’s capacity to precisely comprehend outside data, derive knowledge from that
data, and adaptively apply that knowledge to achieve goals and perform specific tasks [1]. In this sense, AI
technologies have radically changed the field of information technology by developing intelligent devices
and programs that operate and interact in a similar way to humans [2]. It is also one of the most prominent
emerging technologies. Recently, we have witnessed the widespread application of AI in various fields [3].
However, intelligent systems that provide AI capabilities often rely on ML as the basis for their operation.
This technique allows systems to learn from specific training data and automate the creation of analytical
models that enable them to solve relevant tasks [4]. ML algorithms are a well-known technique for analyzing
and predicting data, identifying patterns, and building AI models. Its accuracy has been demonstrated in a
2. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 14, No. 1, February 2024: 1079-1090
1080
number of areas, including education, criminality, and health [5], and the banking industry is no different.
Therefore, AI and ML techniques are increasingly used in the banking industry [6]. In this sense, companies
are increasingly looking to use AI to gain benefits, driven by the availability of substantial amounts of data
and by growing computing power. Nonetheless, they encounter challenges stemming from a limited grasp of
how to generate value and fully leverage AI [7]. In this sense, knowledge of AI, especially ML, is essential to
analyzing and developing intelligent and automated applications based on this data. Within this domain,
various types of ML algorithms are present, encompassing supervised, unsupervised, semi-supervised, and
reinforcement learning [8]. However, for more than a decade, there has been a lot of talk and exploration
about using machine learning techniques. Although researchers have made progress in overcoming the above
limitations, the search for an optimal model remains a challenge in various fields [9]. Nevertheless, these
technologies are revolutionizing the way banks operate, interact with their customers, and make strategic
decisions. AI and ML offer unprecedented potential to improve operational efficiency, enhance financial
services, and deliver personalized customer experiences. But the widespread use of these technologies in the
banking industry raises issues and questions that require an exhaustive review of the existing literature.
As AI and ML become more embedded in banking, there is a need to understand and critically
assess the impact and reach of these technologies. There are numerous studies and research on AI and ML in
banking, but the literature is fragmented and dispersed, making it difficult to gain a systemic and global view
of the trends, challenges, and opportunities. In addition, the rapid evolution of AI and ML requires the
constant updating of information and the identification of gaps and areas for future research. It is essential to
address this issue to provide a solid and up-to-date basis for researchers, professionals, and decision-makers
in the banking sector. Therefore, in this research, a systematic review of the literature (SRL) on AI and ML in
the banking context is carried out. The systemic review will allow the integration, synthesis, and critical
analysis of existing studies, identifying patterns, trends, and gaps in current knowledge. In addition, a
comprehensive update of the literature will be carried out to include the most recent advances in AI and ML
applied to the banking sector. This research will not only provide a comprehensive view of the current
situation but will also serve as a guide for future research and aid in the formulation of well-informed
decisions when ML and AI technologies are implemented in the banking industry.
The importance of this research is to tackle the requirement of a methodical and current
understanding of the function of AI and ML in the banking sector. On the other hand, the main aim of this
study is to carry out a literature review to provide a systematic and updated vision of the advances and to
identify the main applications, algorithmic models, challenges, and opportunities of these technologies,
among others. Likewise, through a critical analysis and an exhaustive synthesis of existing studies, it will
seek to identify emerging trends, future research areas, and best practices for the successful adoption of these
new technologies in the banking industry. By achieving these objectives, it is intended to contribute to the
advancement of knowledge within this domain and establish a solid foundation for well-informed
decision-making in AI and ML-powered banking. It is hoped that this review will enlighten other researchers
interested in this area of study [10]. The structure of the document is divided as follows: section 2 details the
process of the systematic literature review method. While the result and discussion are presented in section 3,
section 4 presents the conclusion of the study.
2. RESEARCH METHOD
2.1. Search strategy
This SRL study followed the guidelines stipulated in the 2020 preferred reporting items for
systematic reviews and meta-analyses (PRISMA) statement, which is widely recognized in the scientific
community as the preferred standard for reporting systematic reviews and meta-analyses. These new
reporting guidelines, which replace the 2009 statement, have considered advances in the methods used to
search for, select, analyze, and synthesize studies [11]. In addition, a comprehensive analysis was performed
according to the three main phases defined in the statement, which included a thorough review of the
scientific literature and the application of strict criteria for the selection of relevant studies as shown in
Figure 1. The data collected and synthesized from the selected studies allowed the research questions to be
adequately addressed.
2.2. Research questions
The SRL is conducted to encompass and analyze existing research. This is accomplished by
categorizing and reviewing existing papers. The first step in the review process is to define the research
questions, which helps to accurately describe the coverage rate of existing papers [10]. Table 1 presents the
research questions formulated to analyze the advances of AI and ML applied in the banking sector. Each of
these questions is identified by the acronym RQ, which stands for “Research Question”.
3. Int J Elec & Comp Eng ISSN: 2088-8708
Financial revolution: a systemic analysis of artificial intelligence and machine … (Raúl Jáuregui-Velarde)
1081
Figure 1. PRISMA 2020 flow diagram of the study
Table 1. Formulated research questions
Id Research questions Purpose
RQ1 What is the general trend of the annual
publication and country with the greatest
contribution in AI and ML in the banking sector?
The answer makes it possible to identify publication trends in the use
of AI and ML in the banking sector, showing the growing interest over
time. It also makes it possible to identify leading countries and
disparities in research in this field.
RQ2 What are the most used keywords or terms
related to AI and ML in the banking sector?
The answer makes it possible to identify the main themes and
underlying structure in the existing literature on AI and ML in banking,
providing a deep understanding of the evolution of key concepts. In
addition, this analysis facilitates the identification of emerging areas of
interest.
RQ3 What are the applications and algorithms of AI
and ML in the banking sector proposed by
different researchers?
The answer makes it possible to identify the AI and ML applications
and algorithms applied by different researchers in the banking industry.
RQ4 What are the applications, algorithms evaluated
and with the highest precision with the highest
level of adoption in the banking sector?
The answer makes it possible to identify the level of adoption of the
applications, the algorithms evaluated more frequently, and the
algorithms with more precise results in each application in the banking
sector.
RQ5 What are the most important advantages and
challenges of AI and ML in the banking sector?
The answer to this question provides an overview of the advantages
and challenges of the use of AI and ML in the banking sector.
2.3. Study selection and eligibility criteria
Generally speaking, all relevant article titles and abstracts were carefully reviewed before being
chosen based on the inclusion and exclusion criteria. Every article should satisfy all inclusion requirements
and none of the established exclusion criteria [12] as shown in Table 2. In this way, only articles from
journals published in English were included, excluding conference proceedings, book chapters, conference
reviews, editorials, and notes. The study period considered was from January 2018 to April 2023, to analyze
the most recent impact of AI and ML in the digital transformation of the banking sector. In addition, only
studies related to the banking sector were included, excluding those carried out in other contexts.
4. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 14, No. 1, February 2024: 1079-1090
1082
Table 2. Eligibility and selection criteria
Criteria Inclusion Exclusion
Document type Article journal Not article journal
Language English Non-English
Timeline January 2018-April 2023 Before January 2018 and after April 2023
Access type Open access Not open access
Context IA and ML in banking sector Other than AI and ML in the banking sector
2.4. Systematic review process
2.4.1. Identification
In the first stage, a thorough keyword search is performed to identify relevant articles. Table 3
shows the search terms used in the three selected bibliographic databases: Scopus, Dimensions, and
EBSCOhost. Initially, a total of 3,245 articles were retrieved in a preliminary search. The primary keys used
for the search were “title, abstract, and keyword” in Scopus, “title and abstract” in Dimensions, and “subject
terms, title, and abstract” in EBSCOhost.
Table 3. Keyword search query main
Search string
(“Artificial intelligence” OR “machine learning”) AND (“banking sector” OR “banking industry” OR
“banking system” OR “bank”)
2.4.2. Screening
This phase was the selection process. The process started with 3,245 articles retrieved in the first
phase, and 23 duplicate articles were eliminated, as shown in Figure 1. Considering the study selection
criteria and using the automated classification tools offered by the databases used, 3,122 articles were
excluded. Because they were not the type of document required for the study, they were published outside the
period considered and written in languages other than English. A total of 100 articles were recovered, of
which 11 could not be recovered due to restricted access. This leaves 89 articles for the eligibility process. To
determine eligibility, an exhaustive reading was carried out, excluding articles without explicit mention in the
abstract as well as review articles, those with irrelevant results, and those that did not address the subject of
the study or were not developed in a banking context.
2.4.3. Inclusion
In this last phase, the quality of the articles eligible for the study was assessed. In this sense, a
checklist was used to assess reliability, which all remaining articles had to meet in order to be included in the
review. Articles must have well-defined methodological procedures, well-defined objectives, and results
supported by evidence. All articles were assessed and discussed by the researchers, who determined that forty
articles met the requirements for inclusion in the review.
3. RESULTS AND DISCUSSION
This SRL aims to provide both researchers and the banking sector with information on the advances
and challenges of AI and ML in order to enhance your understanding and perspective in this field of study.
The results and discussion of the study are presented through the analysis of the collected data, which is
structured according to the research questions (RQs) posed previously, namely, i) general annual and country
publication trend on AI and ML in the banking sector (RQ1), ii) keywords or most used terms related to AI
and ML in the banking sector (RQ2), iii) AI and ML applications and algorithms proposed in the banking
sector by different researchers (RQ3), iv) the level of adoption of AI and ML algorithms and applications in
the banking sector (RQ4), and v) advantages and challenges of implementing AI and ML in banking (RQ5).
3.1. General annual and country publication trend on AI and ML in the banking sector
The yearly output of articles relating to AI and ML in the banking industry has significantly
increased, as shown in Figure 2(a). In the years 2018 and 2019, there was only one publication, indicating an
initial level of interest in the topic under study. However, as of 2020 the quantity of publications has
increased significantly, with eleven publications in each of the years between 2020 and 2022. This sustained
increase shows a growing recognition of the importance of AI and ML in banking. In addition, the fact that
there are already five publications as of April 2023 shows that interest and research in this area continue to
5. Int J Elec & Comp Eng ISSN: 2088-8708
Financial revolution: a systemic analysis of artificial intelligence and machine … (Raúl Jáuregui-Velarde)
1083
grow. Similarly, the steady growth in the number of publications also reflects the advancement of knowledge
in the field and the need to share research and findings to further drive progress. This annual production trend
indicates that AI and ML are becoming increasingly important in the banking system.
On the other hand, Figure 2(b) shows the analysis carried out with the Bibliometrix package and the
graph generated with Microsoft Excel. This analysis highlights the importance of AI and ML in the banking
sector and that research in this area has been particularly prominent in several countries. Among the top 10
contributing countries in terms of published papers on the topic, India leads with more than 25 contributions.
This indicates that the academic and scientific community in India is highly engaged in researching and
developing new applications of AI and ML to improve efficiency and innovation in the banking sector. China
comes in second with ten papers published, underscoring its focus on applying these emerging technologies
in banking. These results demonstrate the growing importance of AI and ML in the banking industry and
highlight the leadership of India and China in generating and advancing knowledge in this field.
(a) (b)
Figure 2. Scientific production in (a) annual production trend of publications included in the study and
(b) top 10 productive countries
3.2. Keywords or most used terms related to AI and ML in the banking sector
Figure 3(a) shows the analysis of the co-occurrence network with VOSviewer software and reveals
the keywords that stand out about the impact of AI and ML. The most prominent terms are ML and AI,
indicating their central relevance to the topic under study. In addition, the presence of specific terms related
to the banking sector is observed, such as credit risk, fraud detection, credit cards, and customer loyalty.
Concepts related to algorithms and learning techniques are also identified, such as random forests (RFs),
decision trees (DTs), and support vector machines (SVMs).
Meanwhile, Figure 3(b) shows the analysis of the terms most used by the authors between 2018 and
April 2023, where it is evident that the progress of AI and ML in the transformation of the banking sector has
been the subject of increased attention in recent years. Among the 10 most used terms in research on this
topic, a slight increase in the use of the terms AI and ML was observed in 2020 and 2021, and their use
continued to increase in 2022. This indicates that researchers and practitioners are increasingly recognizing
the importance of these technologies in banking and are exploring new applications and approaches. The
analysis also shows that the terms credit risk, fraud detection, churn prediction, and DT. have gained
relevance in the context of AI and ML. Although their use was slightly pronounced in 2020 and 2021, a
significant increase in their use was observed in 2022. This suggests that researchers are paying more
attention to the application of these technologies to address challenges related to credit risk management and
fraud detection in banking using different ML algorithms such as DTs and RFs. Similarly, until April 2023,
the terms continue to show an increase in their use, which indicates that research in this area continues to be
active and that the academic and scientific communities are interested in delving into these topics. These
results highlight the importance of these technologies in banking and reflect the need to develop innovative
approaches and efficient tools to improve decision-making in the sector.
3.3. AI and ML applications and algorithms proposed in the banking sector by different researchers
In the last decade, advancements in AI and ML technology have turned into essential instruments,
offering a broad spectrum of uses within the banking sector. In customer segmentation [13]–[15], applied
different models of ML algorithms such as K-means. Likewise, to model and predict potential clients who
intend to obtain loans, Zhang [16] applied the backpropagation neural network (BPNN), the RF, and the
6. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 14, No. 1, February 2024: 1079-1090
1084
SVM. Among these models, the RF algorithm displayed the highest level of accuracy, with an average
prediction accuracy of 94%. Similarly, Koufi et al. [17] have developed an algorithmic model for predicting
potential customers (PCPA). The focus is on predicting full-bank users who might be candidates for
mortgage loans. This predictability gives the marketing department an advantage in targeting potential
customers efficiently and quickly, especially at a low cost. The findings indicate that gradient boosting
outperforms other approaches such as DTs, logistic regression, and SVM. On the other hand, Safarkhani and
Moro [18] developed a highly accurate classifier to forecast the customers who will enroll in a long-term
deposit program provided by a bank, aiding in the identification of potential clients. In tests on real data from
a bank, the DT achieved a prediction accuracy of 94.39%, outperforming other models.
(a)
(b)
Figure 3. Analysis of terms in (a) co-occurrence author’s keywords map and (b) keyword dynamics
On the other hand, Sahu et al. [19] apply AI and ML models to bank fraud detection, particularly
aimed at identifying deceitful credit card transactions. Based on the experimental findings of the different
supervised models, naive bayes and SVM are 97% accurate, and logistic regression is 99% accurate in
detecting credit card fraud. Likewise, Almuteer et al. [20] combine the convolutional neural network (CNN),
auto-encoder (AE), and long short-term memory (LSTM) models for improved performance and detection of
credit card fraud. Converting into four models that can be used: CNN, AE, LSTM, and AE&LSTM. After
training the models, with an overall precision of 0.99, the AE model shows the highest precision, closely
followed by the CNN and LSTM models, both with a precision of 0.85. In contrast, the AE&LSTM model
has the lowest precision, coming in at 0.32. Similarly, Vengatesan et al. [21] used automated classification
7. Int J Elec & Comp Eng ISSN: 2088-8708
Financial revolution: a systemic analysis of artificial intelligence and machine … (Raúl Jáuregui-Velarde)
1085
techniques such as logistic regression and the k-nearest neighbor (KNN) algorithm to predict fraudulent
banking activities. After training, the accuracy value of the KNN algorithm, which is 0.95, gives the best
results. Likewise, other authors have implemented both similar and distinct models, including SVM, logistic
regression, RF, DT, KNN, artificial neural network (ANN), support vector classifier (SVC), extreme gradient
boosting (XGBoost), extra tree (ET), ET-AdaBost, random forest-AdaBost (RF-AdaBost), decision
tree-AdaBoost (DT-AdaBost), among others [22]–[28], as well as an unsupervised model like isolation forest
[29]. On the other hand, Sasikala et al. [30] apply a state-of-the-art ML method to identify fraud in credit
card transactions. They utilize the SVM hyperparameter optimization method, which includes a grid-based
cross-validation search and utilizes kernel replication theory with various functions like linear, Gaussian, and
polynomial to separate the hyperplane. This approach enhances the model’s accuracy.
Similarly, Hayder et al. [31] use AI and ML models for recommendation engines. In addition, they
highlight the importance of bank advertising as an effective way to target customers. They propose a
predictive model that uses ML algorithms, incorporating four classifiers: KNN, DT, naive Bayesian, and
SVM. The DT performed the best, achieving an accuracy of 91%, while the SVM classifier followed closely
with an accuracy of 89%.
Likewise, in study [32], they studied the accuracy of the algorithms called RF and linear regression
to approve bank loans and minimize credit risk. It is observed that the average precision rate of the RF
algorithm has experienced an improvement of up to 70.5% compared to linear regression, which has an
average precision of around 69.5%. Similarly, in the study [33], they applied ML using the KNN model to fill
in the missing information about the unknown credit status in user training. On the other hand, Chen [34]
points out that the increase in customer credit risk, aggravated by the pandemic, has increased the probability
of defaults, which has generated the need to investigate and identify high-risk customers in banking.
Therefore, they propose ML models for prediction. The experimental results demonstrate that both the
improved logistic regression and the ANN model exhibit a clear advantage in predicting credit risk,
depending on the data evaluated. Other authors also develop and compare different models for credit risk
assessment: SVM, DTs, AdaBoost, RF, logistic regression, ANN, and XGBoost. Of these, the different
models with greater predictive accuracy are obtained by each author: AdaBoost and RF [35], DT [36]–[40].
On the other hand, Putri et al. [41] propose to analyze credit risk using SMV. In testing the SVM algorithm,
radial basis functions (RBF), linear, polynomial, and sigmoid were used. Among the four models, the SVM
with a polynomial kernel stands out as the best, as it has the highest accuracy of 0.9508.
3.4. The level of adoption of AI and ML algorithms and applications in the banking sector
After analyzing and identifying the AI and ML applications and algorithms proposed by researchers
in the studies examined, the level of adoption of these applications in banking operations is determined. In
addition, the most evaluated algorithmic models were identified. Likewise, to compare the algorithmic
models that present greater precision, the most valued and categorized models have been selected. In this
way, the study offers a comprehensive analysis to give researchers a deeper understanding of how this
technology is progressing in the banking sector.
3.4.1. Adoption level of AI and ML applications in the banking sector
Figure 4 shows the level of adoption of the most important applications that are generating
significant contributions to the banking field. Fraud detection is an application where AI and ML are widely
implemented, indicating that it is a priority area to prevent fraudulent activities using these technologies.
Similarly, credit risk analysis is another of the applications where technologies such as AI and ML are widely
implemented, indicating that most banks use these technologies to assess the solvency and risk of customers
in their credit decisions. On the other hand, customer segmentation is the application with the lowest
implementation of AI and ML, which indicates that in this area, few banks use these technologies to better
classify and understand their customers. As for the recommendation engine, AI and ML have a low
implementation rate, which could mean that their implementation is not yet so common in banking services.
Overall, these findings indicate a varied uptake of AI and ML within the banking industry, with varying
degrees of deployment in different areas.
3.4.2. Model of algorithms evaluated in the different applications
Figure 5 shows the algorithms evaluated and used in different AI and ML applications by the
different authors in the studies analyzed. In customer segmentation, K-Means, SVM, RF, and DTs stand out
as the most evaluated to group customers into different segments based on similar characteristics. This can
assist businesses in gaining a deeper insight into their clientele and adapting their marketing approaches more
effectively. In fraud detection, a variety of the most frequently evaluated algorithms are observed, such as
RF, logistic regression, SVM, KNN, and DTs, all of which can help identify and prevent fraudulent activity
in various situations. On the other hand, for the recommendation engine, the most common algorithms are
8. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 14, No. 1, February 2024: 1079-1090
1086
KNN, SVM, and DTs, which are evaluated to provide personalized suggestions. Similarly, for credit risk
analysis, DTs, RF, SVM, and logistic regression are the most frequently evaluated algorithms for assessing
the risk associated with credit applicants. These results provide an overview of the most frequently evaluated
and dominant algorithms in each area. Furthermore, these could serve as valuable initial references for
forthcoming projects or investigations concerning AI and ML within this field of study. Nevertheless, it is
crucial to emphasize that the selection of an algorithm is contingent upon the particular issue, the data at
hand, and the goals of the application.
Figure 4. The level of adoption of AI and ML in banking in different areas
Figure 5. Algorithms model evaluated in the different applications
3.4.3. Model of the algorithms with the highest level of precision
Table 4 shows a comparison of algorithms that have shown high accuracy or an area under the curve
(AUC) greater than 80% or 0.80 in various application areas evaluated by various researchers. It is evident
that there is no default algorithm for specific applications; rather, the choice of algorithm depends on data
9. Int J Elec & Comp Eng ISSN: 2088-8708
Financial revolution: a systemic analysis of artificial intelligence and machine … (Raúl Jáuregui-Velarde)
1087
handling and contextual factors. In other words, the higher accuracy of one algorithm does not make it
inherently superior to another, as it is effectiveness depends on the data it analyzes. The accuracy of each
algorithm depends on several factors, such as the size of the data set, the way the data is divided for training
and testing, and the variables under consideration, among others. All these details are available in the
respective papers.
Table 4. Comparison of algorithms in various application areas
Source Application Algorithm model Accuracy AUC
[16] Customer segmentation RF 94% -
[18] Customer segmentation Arbol de decisions 94.39% -
[13] Customer segmentation RF 97% -
[19] Fraud detection LR 99% -
SVM 97% -
Naïve Bayes 97% -
[20] Fraud detection AE 0.99 -
CNN 0.85 -
LSTM 0.85 -
[21] Fraud detection KNN 0.95 -
LR 0.95 -
[22] Fraud detection DT - 0.938
LR - 0.946
SVC - 0.936
KNN - 0.927
Naïve Bayes - 0.908
RF - 0.911
[29] Fraud detection Isolated forest 95.76% -
[23] Fraud detection LR 94.9% -
DT 91.9% -
RF 92.9% -
KNN 93.9% -
[24] Fraud detection KNN 99.95% -
[26] Fraud detection RF 0.9890 0.9886
DT 0.9522 0.9982
XGBoost 0.9853 0.9996
[27] Fraud detection RF-AdaBost 99.95% 1
DT-AdaBoost 99.67% 1
ET-AdaBoost 99.98% 1
XGB-AdaBoost 99.98% 1
[31] Recommendation DT 91% -
SVM 89% -
[37] Credit risk DT 80% -
[34] Credit risk LR 80% -
[41] Credit risk SVM Polynomial 0.9508 0.9419
3.5. Advantages and challenges of implementing AI and ML in banking
Manjaly et al. [42] highlight the immense potential of AI within the banking field, emphasizing that
AI-driven advanced data analysis has the capacity to combat transaction fraud and enhance adherence to
banking regulatory standards. By implementing AI technologies, it is possible to reduce costs and increase
productivity in the banking industry. Likewise, it has great advantages in improving the risk management
practices of the information system and improving banking business performance [43]. It also improves
credit risk management [38], [40], [44], [45]. In credit risk analysis, it would be beneficial not only for
banking institutions but also for the customer, since it would allow them to be more informed about the risk
of not meeting their payments [46], and it also minimizes credit card fraud [45], [47]. It also improves
customer segmentation and increases automated teller machine performance [45]. In addition, the application
of AI and ML in banking can provide customers with personalized, effective, and cost-effective services [48]
Similarly, automating processes with the implementation of AI benefits banking institutions by improving
their profitability and performance, reducing the need for human intervention, and increasing the efficiency
of business processes [49].
However, there are various challenges associated with the application of AI that could undermine
the trust of users or customers in the banking system [48]. Another challenge for AI in banking is that it is
hampered by potential malicious control of large data sets, where hackers can input false information to
influence AI decisions [50]. On the other hand, Ahmed [51] points out that the use of AI has revealed
numerous ethical problems, such as the incorrect evaluation of credit history, the exchange of false
information, and unauthorized operations. Therefore, creating a friendly and ethical AI system is challenging.
10. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 14, No. 1, February 2024: 1079-1090
1088
3.6. Limitation of the study
An important limitation of this study is its narrow data sources. The research team only utilized
three preeminent databases: Dimensions, Scopus, and EBSCOhost. While these platforms offer extensive
coverage of the scientific literature, it remains possible that pertinent research in lesser-known databases was
not included in the review. This limitation may have excluded valuable studies, which could have enhanced
comprehension of the subject matter. Nevertheless, the authors utilized rigorous inclusion criteria and applied
the PRISMA methodology to ensure the quality and coherence of the studies included in the analysis.
4. CONCLUSION
The study concludes that AI and ML technologies have a significant advance in banking. The review
of forty articles selected with the PRISMA 2020 method from databases such as Scopus, Dimensions, and
EBSCOhost revealed that AI and ML are mainly applied in customer segmentation, credit risk analysis, fraud
detection, and recommendation engines that contribute to the transformation of the sector, using algorithmic
models such as DTs, RFs, SVM, logistic regression, and linear regression. Specifically, the study found that
credit risk analysis and fraud detection are the areas where these technologies are most frequently used in
banking. The study’s observations suggest that AI and ML can be valuable tools for credit risk analysis and
management, as well as for credit card fraud prevention and detection. They can also be used to segment
potential banking customers and generate personalized recommendations for services offered by the industry.
In addition to these benefits, ethical challenges associated with handling large amounts of data are also
mentioned. This suggests that while AI and ML offer numerous benefits, it is important to address concerns
related to privacy, security, and ethics in the processing and use of customer data while taking full advantage
of these technologies. This work has opened several questions that require further research on the subject
under study. Further work is needed to explore each of the applications that these technologies present in the
banking field.
REFERENCES
[1] M. Haenlein and A. Kaplan, “A brief history of artificial intelligence: on the past, present, and future of artificial intelligence,”
California Management Review, vol. 61, no. 4, pp. 5–14, Aug. 2019, doi: 10.1177/0008125619864925.
[2] H. Haddad, “The effect of artificial intelligence on the AIS excellence in Jordanian Banks,” Montenegrin Journal of Economics,
vol. 17, no. 4, pp. 155–166, Sep. 2021, doi: 10.14254/1800-5845/2021.17-4.14.
[3] M. Nazar, M. M. Alam, E. Yafi, and M. M. Su’ud, “A systematic review of human-computer interaction and explainable artificial
intelligence in healthcare with artificial intelligence techniques,” IEEE Access, vol. 9, pp. 153316–153348, 2021, doi:
10.1109/ACCESS.2021.3127881.
[4] C. Janiesch, P. Zschech, and K. Heinrich, “Machine learning and deep learning,” Electronic Markets, vol. 31, no. 3, pp. 685–695,
Sep. 2021, doi: 10.1007/s12525-021-00475-2.
[5] J. R. Asor, J. L. Lerios, S. B. Sapin, J. O. Padallan, and C. A. C. Buama, “Fire incidents visualization and pattern recognition
using machine learning algorithms,” Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), vol. 22, no.
3, pp. 1427–1435, Jun. 2021, doi: 10.11591/ijeecs.v22.i3.pp1427-1435.
[6] A. Tammenga, “The application of artificial intelligence in banks in the context of the three lines of defence model,” Maandblad
Voor Accountancy en Bedrijfseconomie, vol. 94, no. 5/6, pp. 219–230, Jun. 2020, doi: 10.5117/mab.94.47158.
[7] I. M. Enholm, E. Papagiannidis, P. Mikalef, and J. Krogstie, “Artificial intelligence and business value: a literature review,”
Information Systems Frontiers, vol. 24, no. 5, pp. 1709–1734, Oct. 2022, doi: 10.1007/s10796-021-10186-w.
[8] I. H. Sarker, “Machine learning: algorithms, real-world applications and research directions,” SN Computer Science, vol. 2, no. 3,
May 2021, doi: 10.1007/s42979-021-00592-x.
[9] S. C. K. Tékouabou, Ş. C. Gherghina, H. Toulni, P. N. Mata, M. N. Mata, and J. M. Martins, “A machine learning framework
towards bank telemarketing prediction,” Journal of Risk and Financial Management, vol. 15, no. 6, Jun. 2022, doi:
10.3390/jrfm15060269.
[10] Z. Z. Zulkipli, R. Maskat, and N. H. I. Teo, “A systematic literature review of automatic ontology construction,” Indonesian
Journal of Electrical Engineering and Computer Science (IJEECS), vol. 28, no. 2, pp. 878–889, Nov. 2022, doi:
10.11591/ijeecs.v28.i2.pp878-889.
[11] M. J. Page et al., “The PRISMA 2020 statement: an updated guideline for reporting systematic reviews,” BMJ, vol. 372, Mar.
2021, doi: 10.1136/bmj.n71.
[12] N. U. C. Mustaffa and S. N. Sailin, “A systematic review of mobile-assisted language learning research trends and practices in
Malaysia,” International Journal of Interactive Mobile Technologies (iJIM), vol. 16, no. 5, pp. 169–198, Mar. 2022, doi:
10.3991/ijim.v16i05.28129.
[13] H. D. Tran, N. Le, and V.-H. Nguyen, “Customer churn prediction in the banking sector using machine learning-based
classification models,” Interdisciplinary Journal of Information, Knowledge, and Management, vol. 18, pp. 87–105, 2023, doi:
10.28945/5086.
[14] E. Umuhoza, D. Ntirushwamaboko, J. Awuah, and B. Birir, “Using unsupervised machine learning techniques for behavioral-
based credit card users segmentation in Africa,” SAIEE Africa Research Journal, vol. 111, no. 3, pp. 95–101, Sep. 2020, doi:
10.23919/SAIEE.2020.9142602.
[15] D. I. Durojaye and G. Obunadike, “Analysis and visualization of market segmentation in banking sector using k-means machine
learning algorithm,” Fudma Journal of Sciences, vol. 6, no. 1, pp. 387–393, Apr. 2022, doi: 10.33003/fjs-2022-0601-910.
[16] M. Zhang, “Research on precision marketing based on consumer portrait from the perspective of machine learning,” Wireless
Communications and Mobile Computing, pp. 1–10, May 2022, doi: 10.1155/2022/9408690.
11. Int J Elec & Comp Eng ISSN: 2088-8708
Financial revolution: a systemic analysis of artificial intelligence and machine … (Raúl Jáuregui-Velarde)
1089
[17] N. El Koufi, A. Belangour, and M. Sdiq, “Research on precision marketing based on big data analysis and machine learning: case
study of Morocco,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 10, 2022, doi:
10.14569/IJACSA.2022.0131008.
[18] F. Safarkhani and S. Moro, “Improving the accuracy of predicting bank depositor’s behavior using a decision tree,” Applied
Sciences, vol. 11, no. 19, Sep. 2021, doi: 10.3390/app11199016.
[19] S. Sahu, D. shikha Agarawal, and D. R. Baraskar, “The effect of best first search optimization on credit card fraudulent
transaction detection,” International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 12, pp. 1939–1946,
Oct. 2019, doi: 10.35940/ijitee.L2894.1081219.
[20] A. H. Almuteer, A. A. Aloufi, W. O. Alrashidi, J. F. Alshobaili, and D. M. Ibrahim, “Detecting credit card fraud using machine
learning,” International Journal of Interactive Mobile Technologies (iJIM), vol. 15, no. 24, pp. 108–122, Dec. 2021, doi:
10.3991/ijim.v15i24.27355.
[21] K. Vengatesan, A. Kumar, S. Yuvraj, V. D. Ambeth Kumar, and S. S. Sabnis, “Credit card fraud detection using data analytics
techniques,” Advances in Mathematics: Scientific Journal, vol. 9, no. 3, pp. 1177–1188, Jun. 2020, doi: 10.37418/amsj.9.3.43.
[22] B. Mytnyk, O. Tkachyk, N. Shakhovska, S. Fedushko, and Y. Syerov, “Application of artificial intelligence for fraudulent
banking operations recognition,” Big Data and Cognitive Computing, vol. 7, no. 2, May 2023, doi: 10.3390/bdcc7020093.
[23] G. B. Chowdari, “Supervised machine learning algorithms for detecting credit card fraud,” EPRA International Journal of
Research and Development (IJRD), pp. 131–134, Jul. 2021, doi: 10.36713/epra7636.
[24] J. Parmar, A. C. Patel, and M. Savsani, “Credit card fraud detection framework-a machine learning perspective,” International
Journal of Scientific Research in Science and Technology, pp. 431–435, Dec. 2020, doi: 10.32628/IJSRST207671.
[25] P. Sharma, S. Banerjee, D. Tiwari, and J. C. Patni, “Machine learning model for credit card fraud detection-a comparative
analysis,” The International Arab Journal of Information Technology, 2021, doi: 10.34028/iajit/18/6/6.
[26] M. Sudhakar and K. P. Kaliyamurthie, “A novel machine learning algorithms used to detect credit card fraud transactions,”
International Journal on Recent and Innovation Trends in Computing and Communication, vol. 11, no. 2, pp. 163–168, Mar.
2023, doi: 10.17762/ijritcc.v11i2.6141.
[27] E. Ileberi, Y. Sun, and Z. Wang, “Performance evaluation of machine learning methods for credit card fraud detection using
SMOTE and AdaBoost,” IEEE Access, vol. 9, pp. 165286–165294, 2021, doi: 10.1109/ACCESS.2021.3134330.
[28] P. S. Kumar, Preethika, Sivagami, Sridevipriya, and Vishali, “Credit card fraudulent detection using machine learning algorithm,”
International Journal for Research in Engineering Application and Management, pp. 445–449, Apr. 2020, doi: 10.35291/2454-
9150.2020.0330.
[29] D. Kawade, S. Lalge, and D. M. Bharati, “Fraud detection in credit card data using unsupervised machine learning algorithm,”
International Journal for Research in Applied Science and Engineering Technology, vol. 10, no. 5, pp. 5249–5256, May 2022,
doi: 10.22214/ijraset.2022.42974.
[30] G. Sasikala et al., “An innovative sensing machine learning technique to detect credit card frauds in wireless communications,”
Wireless Communications and Mobile Computing, pp. 1–12, Jun. 2022, doi: 10.1155/2022/2439205.
[31] I. M. Hayder, G. A. N. Al Ali, and H. A. Younis, “Predicting reaction based on customer’s transaction using machine learning
approaches,” International Journal of Electrical and Computer Engineering (IJECE), vol. 13, no. 1, pp. 1086–1096, Feb. 2023,
doi: 10.11591/ijece.v13i1.pp1086-1096.
[32] C. V. Sandeep and T. Devi, “A novel approach for bank loan approval by verifying background information of customers through
credit score and analyze the prediction accuracy using random forest over linear regression algorithm,” Journal of Pharmaceutical
Negative Results, vol. 13, pp. 1748–1755, Jan. 2022, doi: 10.47750/pnr.2022.13.S04.211.
[33] W. Yan et al., “A deep machine learning-based assistive decision system for intelligent load allocation under unknown credit
status,” Computational Intelligence and Neuroscience, pp. 1–8, Sep. 2022, doi: 10.1155/2022/5932554.
[34] H. Chen, “Prediction and analysis of financial default loan behavior based on machine learning model,” Computational
Intelligence and Neuroscience, pp. 1–10, Sep. 2022, doi: 10.1155/2022/7907210.
[35] M. C. Aniceto, F. Barboza, and H. Kimura, “Machine learning predictivity applied to consumer creditworthiness,” Future
Business Journal, vol. 6, no. 1, Dec. 2020, doi: 10.1186/s43093-020-00041-w.
[36] F. Doko, S. Kalajdziski, and I. Mishkovski, “Credit risk model based on central bank credit registry data,” Journal of Risk and
Financial Management, vol. 14, no. 3, Mar. 2021, doi: 10.3390/jrfm14030138.
[37] S. Kokate and M. S. R. Chetty, “Credit risk assessment of loan defaulters in commercial banks using voting classifier ensemble
learner machine learning model,” International Journal of Safety and Security Engineering, vol. 11, no. 5, pp. 565–572, Oct.
2021, doi: 10.18280/ijsse.110508.
[38] B. Sabeti, H. A. Firouzjaee, R. Fahmi, S. Safavi, W. Wang, and M. D. Plumbley, “Credit risk rating using state machines and
machine learning,” International Journal of Trade, Economics and Finance, vol. 11, no. 6, pp. 163–168, Dec. 2020, doi:
10.18178/ijtef.2020.11.6.683.
[39] A. Ghazaryan, L. Grigoryan, and G. Arakelyan, “Implementation of machine learning in the credit risk management system of
individuals,” Messenger of Armenian State University of Economics, pp. 123–138, 2022, doi: 10.52174/1829-0280_2022.5-123.
[40] J. Fan, “Predicting of credit default by SVM and decision tree model based on credit card data,” BCP Business and Management,
vol. 38, pp. 28–33, Mar. 2023, doi: 10.54691/bcpbm.v38i.3666.
[41] N. H. Putri, M. Fatekurohman, and I. M. Tirta, “Credit risk analysis using support vector machines algorithm,” Journal of
Physics: Conference Series, vol. 1836, no. 1, Mar. 2021, doi: 10.1088/1742-6596/1836/1/012039.
[42] J. Manjaly, R. M. Varghese, and P. Varughese, “Artificial intelligence in the banking sector-a critical analysis,” Shanlax
International Journal of Management, vol. 8, pp. 210–216, Feb. 2021, doi: 10.34293/management.v8iS1-Feb.3778.
[43] H. Lee, “Role of artificial intelligence and enterprise risk management to promote corporate entrepreneurship and business
performance: evidence from korean banking sector,” Journal of Intelligent and Fuzzy Systems, vol. 39, no. 4, pp. 5369–5386, Oct.
2020, doi: 10.3233/JIFS-189022.
[44] N. Milojević and S. Redzepagic, “Prospects of artificial intelligence and machine learning application in banking risk
management,” Journal of Central Banking Theory and Practice, vol. 10, no. 3, pp. 41–57, 2021, doi: 10.2478/jcbtp-2021-0023.
[45] S. M. Tang and H. N. Tien, “Impact of artificial intelligence on Vietnam commercial bank operations,” International Journal of
Social Science and Economics Invention, vol. 6, no. 7, pp. 296–303, Jul. 2020, doi: 10.23958/ijssei/vol06-i07/216.
[46] R. Charan, K. Kaushanthi Devi, G. Sai Teja, G. Sai Siddhartha, and D. Sitharam, “Credit card fraud detection using Ml
algorithms,” International Journal of Advanced Research in Science, Communication and Technology, pp. 285–289, Nov. 2022,
doi: 10.48175/IJARSCT-7636.
[47] A. K. Joshi, V. Shirol, S. Jogar, P. Naik, and A. Yaligar, “Credit card fraud detection using machine learning techniques,”
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, pp. 436–442, May
2020, doi: 10.32628/CSEIT2063114.
12. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 14, No. 1, February 2024: 1079-1090
1090
[48] A. Lui and G. W. Lamb, “Artificial intelligence and augmented intelligence collaboration: regaining trust and confidence in the
financial sector,” Information and Communications Technology Law, vol. 27, no. 3, pp. 267–283, Sep. 2018, doi:
10.1080/13600834.2018.1488659.
[49] K. Ris, Ž. Stanković, and Z. Ž. Avramović, “Implications of implementation of artificial intelligence in the banking business in
relation to the human factor,” JITA-Journal of Information Technology and Applications (Banja Luka)-APEIRON, vol. 19, no. 1,
Mar. 2020, doi: 10.7251/JIT2001049R.
[50] K. Srivastava, “Paradigm shift in Indian banking industry with special reference to artificial intelligence,” Turkish Journal of
Computer and Mathematics Education (TURCOMAT), vol. 12, no. 5, pp. 1623–1629, Apr. 2021, doi:
10.17762/turcomat.v12i5.2139.
[51] F. Ahmed, “Ethical aspects of artificial intelligence in banking,” Journal of Research in Economics and Finance Management,
vol. 1, no. 2, pp. 55–63, Dec. 2022, doi: 10.56596/jrefm.v1i2.7.
BIOGRAPHIES OF AUTHORS
Raúl Jáuregui-Velarde has a Bachelor’s degree in Systems and Computer
Engineering. He has a background in computer system design, with a focus on artificial
intelligence applications, machine learning, and data science. His research interests are in the
area of computer science. He can be contacted at email: jaureguivraul@gmail.com.
Laberiano Andrade-Arenas Doctor in Systems and Computer Engineering.
Master in Systems Engineering. Graduated with a Master’s Degree in University Teaching.
Graduated with a Master’s degree in accreditation and evaluation of educational quality.
Systems Engineer. scrum fundamentals certified, a research professor with publications in
Scopus-indexed journals. He can be contacted at email: landrade@uch.edu.pe.
Pedro Molina-Velarde he is a Systems Engineer, with a Master’s degree in
administration. Software development specialist; he is also an undergraduate teacher at private
universities. He can be contacted at email: pmolina@utp.edu.pe.
Cesar Yactayo-Arias obtained a bachelor’s degree in administration from
Universidad Inca Garcilazo de la Vega and a master’s degree in education from Universidad
Nacional de Educación Enrique Guzmán y Valle, he is a doctoral candidate in administration
at Universidad Nacional Federico Villarreal. Since 2016 he has been teaching administration
and mathematics subjects at the Universidad de Ciencias y Humanidades and since 2021 at the
Universidad Continental. Currently, he also works as an administrator of educational services
at the higher level, he is the author and co-author of several refereed articles in journals, and
his research focuses on TIC applications to education, as well as management using computer
science and the internet. He can be contacted at the following e-mail address:
yactayocesar@gmail.com.