1. The document describes a project that aims to develop a machine learning model for credit card fraud detection. It involves gathering credit card transaction data, preprocessing the data, and using algorithms like decision trees, logistic regression, and random forests to classify transactions as fraudulent or legitimate.
2. The objectives are to accurately identify fraudulent transactions in real-time to prevent financial losses for cardholders and institutions. This would enhance security and protect stakeholders.
3. A literature review is presented on papers discussing credit card fraud detection techniques using machine learning algorithms. The feasibility, scope, requirements, architecture, algorithms, and diagrams of the proposed system are outlined.
Fraud detection is a topic which is applicable to many industries including banking and financial sectors, insurances, government agencies, and low enforcement and more.Through the use of sophisticeted use of data mining tools, millions of transactions can be searched to spot patterns and detect fraudulent transactions.
Its a process of identifying fraudulent transaction.
This technique used to recognize fraudulent creddit card transactions so that customers are not charged for items that they did not purchases
Fraud detection is a topic which is applicable to many industries including banking and financial sectors, insurances, government agencies, and low enforcement and more.Through the use of sophisticeted use of data mining tools, millions of transactions can be searched to spot patterns and detect fraudulent transactions.
Its a process of identifying fraudulent transaction.
This technique used to recognize fraudulent creddit card transactions so that customers are not charged for items that they did not purchases
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
call for paper 2012, hard copy of journal, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal,
The patent US11611582B2 has bestowed upon us a computer-implemented method that uses a pre-defined statistical model to detect phishing threats. Because, you know, phishing is such a novel concept that we've never thought to guard against it before.
This method, a dazzling spectacle of machine learning wizardry, dynamically analyzes network requests in real-time. It's not just any analysis, though—it's proactive! That means it actually tries to stop phishing attacks before they happen, unlike those other lazy methods that just sit around waiting for disaster to strike.
When a network request graciously makes its way to our system, it must first reveal its secrets—things like the fully qualified domain name, the domain's age (because older domains clearly have more wisdom), the domain registrar, IP address, and even its geographic location. Because obviously, geographic location is crucial. Everyone knows that phishing attacks from scenic locations are less suspicious.
These juicy details are then fed to the ever-hungry, pre-trained statistical model, which, in its infinite wisdom, calculates a probability score. This score, a beacon of numerical judgment, tells us the likelihood that this humble network request is actually a wolf in sheep's clothing, a.k.a. a phishing threat.
And should this score dare exceed the sanctity of our pre-defined threshold—an arbitrary line in the cyber sand—an alert is generated. Because nothing says "I'm on top of things" like a good old-fashioned alert.
This statistical model isn't some static relic; it's a living, learning creature. It's trained on datasets teeming with known phishing and non-phishing examples and is periodically updated with fresh data to keep up with the ever-evolving fashion trends of phishing attacks.
Truly, we are blessed to have such an innovative tool at our disposal, tirelessly defending our digital realms from the ceaseless onslaught of phishing attempts. What would we do without it? Probably just use common sense, but where's the fun in that?
-----
This document will provide a analysis of patent US11611582B2, which describes a computer-implemented method for detecting phishing threats. The analysis will cover various aspects of the patent, including its technical details, potential applications, and implications for cybersecurity professionals and other industry sectors.
Furthermore, it has a relevance to the evolving landscape of DevSecOps underscores its potential to contribute to more secure and efficient software development lifecycles as it offers a methodical approach to phishing detection that can be adopted by various tools and services to safeguard users and organizations from malicious online activities. Cybersecurity professionals should consider integrating such methods into their defensive strategies to stay ahead of emerging threats.
Dive into the intricate world of fraud detection with this comprehensive presentation featuring an unique student project. Explore the project's objectives, methodologies, and innovative solutions developed to combat fraudulent activities within financial transactions. From data analysis to model implementation, witness the journey our student has undertaken to create a robust fraud detection system. Whether you're a fellow student, industry professional, or enthusiast, this showcase provides valuable insights into the challenges and advancements in fraud detection technology. To learn more, do check out https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/.
This deck is from Interpol Conference 2017, these slides shows the holistic view of machine learning in cyber security for better organization readiness
ghtyfvgyhuohikbjgcfgvhkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkAir pollution is the act of mixing pollutants into air which is not ideal because it decreases the quality of life of human-beings and affects the overall planet’s habitat. Air pollution occurs when dangerous particles, gases, and chemicals are released into the air. The pollutants of air can be found in vehicle exhausts, fumes from factories and power plants, and construction sites. Respiratory problems, skin diseases, irritation of the eyes are some of the major health issues caused by air pollution. To combat this, many governments have created and enforced policies to reduce air pollution, such as shutting down coal power plants or requiring car owners to switch over to electric cars. Air purifiers are being installed at points of high vehicular movement. Rain seeding is another step to clean up the air. We should also plant more trees and care for them as trees filter pollutants and absorb carbon dioxide. Air Pollution is a challenge that humankind needs to overcome to see a better tomorrow.
(166 words)
Example 2: Importance of Trees
Trees are very important, valuable and necessary to our existence as they have furnished us with two important life essentials; food and oxygen. Trees intake Carbon dioxide from air and breathe out fresh oxygen. Carbon dioxide breathed in by the trees is one of the greenhouse gases. So planting more trees will clean the air and reduce the ill – effects of global warming.
Trees provide food to man and all herbivorous animals. Animals, insects, birds, and fungi make their home in the trees and make a diverse ecosystem. Trees also help in binding the soil. When trees are cut off, the most fertile top soil layer gets washed away easily in rains or floods. Trees provide us with medicinal herbs, timber, shelter too.
Hence, We should encourage planting more and more trees. It is for our own betterment and the sooner we understand this, the better it is for us.
(150 words)
Example 3: India of my Dreams
India is a country where people of all cultures and religions coexist. As Indian citizens, we are continuously looking for ways to improve our country and see a better India.
In the India of my dreams, women would be safe and be able to travel freely. Additionally, it will be a place where everyone may experience freedom and equality in its truest form. It would also be a place without caste, colour, gender, creed, social or economic standing, or race prejudice. India of my dreams should be a place where poor people get empowerment, face no poverty, do not starve, and get the proper roof to live. Additionally, I think of it as a place that experiences a lot of technological growth and development. I wish our wonderful nation nothing ggggggggg
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
call for paper 2012, hard copy of journal, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal,
The patent US11611582B2 has bestowed upon us a computer-implemented method that uses a pre-defined statistical model to detect phishing threats. Because, you know, phishing is such a novel concept that we've never thought to guard against it before.
This method, a dazzling spectacle of machine learning wizardry, dynamically analyzes network requests in real-time. It's not just any analysis, though—it's proactive! That means it actually tries to stop phishing attacks before they happen, unlike those other lazy methods that just sit around waiting for disaster to strike.
When a network request graciously makes its way to our system, it must first reveal its secrets—things like the fully qualified domain name, the domain's age (because older domains clearly have more wisdom), the domain registrar, IP address, and even its geographic location. Because obviously, geographic location is crucial. Everyone knows that phishing attacks from scenic locations are less suspicious.
These juicy details are then fed to the ever-hungry, pre-trained statistical model, which, in its infinite wisdom, calculates a probability score. This score, a beacon of numerical judgment, tells us the likelihood that this humble network request is actually a wolf in sheep's clothing, a.k.a. a phishing threat.
And should this score dare exceed the sanctity of our pre-defined threshold—an arbitrary line in the cyber sand—an alert is generated. Because nothing says "I'm on top of things" like a good old-fashioned alert.
This statistical model isn't some static relic; it's a living, learning creature. It's trained on datasets teeming with known phishing and non-phishing examples and is periodically updated with fresh data to keep up with the ever-evolving fashion trends of phishing attacks.
Truly, we are blessed to have such an innovative tool at our disposal, tirelessly defending our digital realms from the ceaseless onslaught of phishing attempts. What would we do without it? Probably just use common sense, but where's the fun in that?
-----
This document will provide a analysis of patent US11611582B2, which describes a computer-implemented method for detecting phishing threats. The analysis will cover various aspects of the patent, including its technical details, potential applications, and implications for cybersecurity professionals and other industry sectors.
Furthermore, it has a relevance to the evolving landscape of DevSecOps underscores its potential to contribute to more secure and efficient software development lifecycles as it offers a methodical approach to phishing detection that can be adopted by various tools and services to safeguard users and organizations from malicious online activities. Cybersecurity professionals should consider integrating such methods into their defensive strategies to stay ahead of emerging threats.
Dive into the intricate world of fraud detection with this comprehensive presentation featuring an unique student project. Explore the project's objectives, methodologies, and innovative solutions developed to combat fraudulent activities within financial transactions. From data analysis to model implementation, witness the journey our student has undertaken to create a robust fraud detection system. Whether you're a fellow student, industry professional, or enthusiast, this showcase provides valuable insights into the challenges and advancements in fraud detection technology. To learn more, do check out https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/.
This deck is from Interpol Conference 2017, these slides shows the holistic view of machine learning in cyber security for better organization readiness
ghtyfvgyhuohikbjgcfgvhkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkAir pollution is the act of mixing pollutants into air which is not ideal because it decreases the quality of life of human-beings and affects the overall planet’s habitat. Air pollution occurs when dangerous particles, gases, and chemicals are released into the air. The pollutants of air can be found in vehicle exhausts, fumes from factories and power plants, and construction sites. Respiratory problems, skin diseases, irritation of the eyes are some of the major health issues caused by air pollution. To combat this, many governments have created and enforced policies to reduce air pollution, such as shutting down coal power plants or requiring car owners to switch over to electric cars. Air purifiers are being installed at points of high vehicular movement. Rain seeding is another step to clean up the air. We should also plant more trees and care for them as trees filter pollutants and absorb carbon dioxide. Air Pollution is a challenge that humankind needs to overcome to see a better tomorrow.
(166 words)
Example 2: Importance of Trees
Trees are very important, valuable and necessary to our existence as they have furnished us with two important life essentials; food and oxygen. Trees intake Carbon dioxide from air and breathe out fresh oxygen. Carbon dioxide breathed in by the trees is one of the greenhouse gases. So planting more trees will clean the air and reduce the ill – effects of global warming.
Trees provide food to man and all herbivorous animals. Animals, insects, birds, and fungi make their home in the trees and make a diverse ecosystem. Trees also help in binding the soil. When trees are cut off, the most fertile top soil layer gets washed away easily in rains or floods. Trees provide us with medicinal herbs, timber, shelter too.
Hence, We should encourage planting more and more trees. It is for our own betterment and the sooner we understand this, the better it is for us.
(150 words)
Example 3: India of my Dreams
India is a country where people of all cultures and religions coexist. As Indian citizens, we are continuously looking for ways to improve our country and see a better India.
In the India of my dreams, women would be safe and be able to travel freely. Additionally, it will be a place where everyone may experience freedom and equality in its truest form. It would also be a place without caste, colour, gender, creed, social or economic standing, or race prejudice. India of my dreams should be a place where poor people get empowerment, face no poverty, do not starve, and get the proper roof to live. Additionally, I think of it as a place that experiences a lot of technological growth and development. I wish our wonderful nation nothing ggggggggg
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
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In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
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Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
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It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
1. AJEENKYA D Y PATIL SCHOOL OF ENGINEERING
Department of Computer Engineering
BE Project
Semester-II A.Y. 2022-23
Project Group ID : 29
Team Members PRN number
1. SANKET SUNIL KADAM 72034780H
2. AJAY RAJNDRA THENGALE 72034817L
3. AJAY VINOD MALI 72034715H
4. PRANALI POPAT CHAUDHARI 72034647K
Project Title : CREDIT CARD FRAUD DETRCTION USING MACHINE
LEARNING
Project Guide : Prof. Ishwar Bharambe
Area of Project : Machine Learning
2. Contents
Problem Statement
Motivation
Objectives
Introduction to Domain
Literature Review
Project Feasibility
Scope of Project
Mathematical Model
Software Hardware Requirements
System Architecture
Algorithm
Structural Diagrams
Behavioral Diagrams
Advantages, Limitations, Applications
Conclusion
Future Work
References in Standard Format (At least 15, starting with Base Paper and in Year wise Decreasing Order)
3. The problem we aim to address is credit card fraud detection. This is a significant
concern in the financial industry, where unauthorized transactions can lead to
substantial financial losses for both cardholders and financial institutions. The
objective is to develop an accurate and efficient system that can identify fraudulent
credit card transactions in real-time, preventing financial losses and ensuring the
security of cardholders' accounts.
2.1 Why is it Used?
• To safeguard cardholders
Credit card fraud detection systems help protect cardholders from unauthorized
transactions and potential financial losses.
• To minimize financial impact
Financial institutions can utilize these systems to reduce the financial impact of
fraudulent activities on their business operations.
• To ensure transaction security
By accurately identifying fraudulent transactions, the system ensures the
security and integrity of credit card transactions.
1.INTRODUCTION
2. Motivation
4. 2.2 Who Benefits it?
• Financial Institutions: Banks, credit card companies, and other financial institutions
utilize credit card fraud detection systems to protect their customers and their own
financial assets.
• Cardholders: Credit cardholders benefit from these systems as they provide an additional
layer of security to prevent unauthorized transactions.
2.3 How is it Used?
• Real-time monitoring: The system continuously analyzes credit card transactions in real-
time, identifying suspicious patterns or anomalies that may indicate fraudulent activity.
• Machine Learning Algorithms: Advanced machine learning algorithms are employed to
analyze transaction data, detect patterns, and make accurate predictions about the
likelihood of fraud.
• Integration with existing systems: The credit card fraud detection system is seamlessly
integrated into the existing infrastructure of financial institutions, allowing for efficient and
effective monitoring and prevention of fraudulent transactions.
6. • The credit card fraud detection project operates in the domain
of financial security and fraud prevention within the credit
card industry.
• This domain focuses on developing robust systems and
algorithms to detect and prevent fraudulent transactions,
protecting both financial institutions and cardholders from
financial losses and unauthorized activities.
• By leveraging advanced machine learning techniques and real-
time data analysis, the project aims to enhance the security
measures in place and provide a reliable and efficient solution
to combat credit card fraud.
4.
Introduction
To
Domain
7. 5. Literature Review
• Paper 1: A Survey Paper on Credit Card Fraud Detection Techniques.
* Authors:-
Aisha Mohammad Fayyomi, Derar Eleyan, Amina Eleyan
This paper addresses the growing concern of credit card fraud due to
the widespread use of electronic transactions.
• Paper 2: Literature Review of Different Machine Learning Algorithms for
Credit Card Fraud Detection.
* Authors:-
Nayan Uchhana, Ravi Ranjan, Shashank Sharma, Deepak
Agrawal, Anurag Punde.
It emphasizes the need for, credit card companies to implement
effective technologies and systems to detect and prevent fraud.
comparative analysis of their effectiveness.
8. • Paper 3: A Review on Credit Card Fraud Detection Techniques.
* Author:
Reetu Elza Joseph.
The paper reviews machine learning and deep learning algorithms used
for credit card fraud detection, addressing the increasing occurrence of
credit card fraud in the context of online transactions and ecommerce
platforms.
• Paper 4: Review of Machine Learning Approach on Credit Card Fraud Detection
* Authors:
Rejwan Bin Sulaiman Vitaly, Schetinin Paul Sant.
Research article focuses on the increasing credit card fraud due to the
widespread usage of credit cards and the growth of online businesses.
9. 1. Technical Feasibility:
Assess if the required technologies, programming languages, and computational
resources:
1. Python Programming Language
2. Python Libraries(like NumPy, Pandas, Sklearn ,etc)
3. Machine Learning Algorithms (Decision Tree, Logistic Regression,
Random Forest.
2. Data Feasibility:
Assess the availability of Data:
1. Transaction Data.
2. Available in PCA form.
3. Schedule Feasibility:
Project timeline:
1. Data Pre-processing
2. Model Training
3. Model Evaluation
4. User Interface
6. Project Feasibility
10. 7. Scope of Project
Our Scope of project includes,
1. Develop a machine learning-based system to detect fraudulent credit card
transactions in real-time.
2. Gather and pre-process historical and real-time transaction data to train and
test the fraud detection models.
3. Implement and evaluate multiple machine learning algorithms such as
decision tree, logistic regression, and random forest.
The mathematical model for credit card fraud detection involves
using machine learning algorithms is:
y = f(x)
Where:
- y represents the predicted class label (fraudulent or legitimate)
- x represents the input features (transaction attributes such as
transaction method, time, amount, etc.)
- f represents the decision function learned by the machine learning algorithm
8. MATHAMATICAL MODEL
11. Software Requirements:
- Operating System: Windows, Linux, or macOS
- Python version 3.7 or above
- Python libraries: scikit-learn, pandas, NumPy, Flask, HTML/CSS
Hardware Requirements:
- Processor: Intel Core i5 or equivalent
- RAM: 8 GB or higher
- Storage: 500 GB hard disk or SSD
- Internet connection for data access and model training
- Web browser for accessing the user interface
9. SOFTWARE AND HARDWARE REQUIRENMENT
12. 1. Decision Tree:
- Each internal node in the decision tree represents a decision
rule based on a specific feature and its threshold value.
- For example: if Transaction Amount <= $100, go to the left
child node; otherwise, go to the right child node.
2. Logistic Regression:
- Logistic Function (Sigmoid): The logistic regression model
uses a sigmoid function to map the input features to a
probability value between 0 and 1.
3. Random Forest:
- Ensemble of Decision Trees: Random Forest is an ensemble
method that combines multiple decision trees.
10. ALGORITHMS
15. Advantages:
1. Improved Accuracy:
Machine learning algorithms can analyse large volumes of transaction data
and identify patterns that may not be easily detectable by traditional rule-
based systems.
2. Real-Time Detection:
Machine learning models can provide real-time detection of fraud by
quickly analysing transaction data.
Limitations :
1. False Positives:
Machine learning models may occasionally classify legitimate transactions
as fraudulent.
2. Imbalanced Data:
Credit card fraud datasets are often imbalanced, with a majority of legitimate
transactions and a small number of fraudulent one.
14. ADVANTAGES, LIMITATIONS
17. - Credit card fraud detection using machine learning is an effective approach
for identifying fraudulent transactions.
- Machine learning algorithms such as decision trees, logistic regression, and
random forest can analyse transaction data and classify transactions as
fraudulent or legitimate.
- The project successfully implemented a web application using Flask and
HTML, allowing users to input transaction details and obtain fraud
predictions in real-time.
- The evaluation of the models showed promising precision, recall, and F1-
score values, indicating their ability to accurately identify fraud while
minimizing false positives.
16. CONCLUSION
18. 1. Ensemble Methods:
Investigate the effectiveness of ensemble methods, such as stacking or boosting, to
further improve the performance of the fraud detection system.
2. Deep Learning Approaches:
Explore the application of deep learning techniques, such as neural networks or
recurrent neural networks, to capture complex patterns and relationships.
3. Unsupervised Learning Techniques:
Investigate unsupervised learning methods, such as clustering algorithms or
anomaly detection, to detect fraudulent transactions without relying solely on
labelled data.
4. Explainable AI:
Enhance the interpretability of the machine learning models used for fraud
detection, enabling better understanding of the decision-making process and
providing actionable insights for fraud investigation and prevention.
17. FUTURE WORK
19. 18. REFERENCES
[1] “Fraud Detection in Credit Cards using Logistic Regression” ,
Hala Z Alenzi1, Nojood O Aljehane,
(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 11, No. 12, 2020
[2] “Credit Card Fraud Detection using Machine Learning and Data Science” ,
Aditya Saini, Swarna Deep Sarkar Shadab Ahmed ,
International Journal of Engineering Research & Technology (IJERT) http://www.ijert.org
ISSN: 2278-0181 IJERTV8IS090031 (This work is licensed under a Creative Commons
Attribution 4.0 International License.) Issue 09 September-2019
[3] “Credit Card Fraud Detection using Machine Learning Algorithms’’,
Vaishnavi Nath Dornadulaa , Geetha Sa
- INTERNATIONAL CONFERENCE ON RECENT TRENDS IN
ADVANCED COMPUTING 2019, ICRTAC 2019
[4] “Credit Card Fraud Detection System”
Kartik Madkaikar, Manthan Nagvekar, Preity Parab, Riya Raikar, Supriya Patil,
International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878
(Online), Volume-10 Issue-2