The document discusses how AI and machine learning can help detect, predict, and prevent fraud by analyzing large amounts of transaction data using predictive models, which can identify patterns and behaviors across different business lines to more accurately detect fraudulent activities in real time. It also highlights the challenges of fraud detection including data silos, data overload from multiple channels and fraud types, and the need for a platform to provide collaboration and a single view of insights.
In this new Accenture Finance & Risk presentation we explore machine learning as a solution to some of the most important challenges faced by the banking sector today. To learn more, read our blog on Machine Learning in Banking: https://accntu.re/2oTVJiX
Artificial intelligence applications are increasingly being used in the financial sector. Chatbots can help reduce costs by automating some customer service tasks, while machine learning algorithms can help make know-your-customer processes more efficient by identifying patterns in transaction data. Artificial intelligence may also allow for more accurate foreign exchange price predictions and personalized robo-advisor services. These applications demonstrate how artificial intelligence is disrupting traditional financial services.
Machine Learning (ML) for Fraud Detection.
- fraud is a big problem (big data, big cost)
- ML on bigger data produces better results
- Industry standard today (for detecting fraud)
- How to improve fraud detection!
This document describes a student project to build a machine learning model to predict loan eligibility. A group of 4 students created a model using random forest classification that achieved 77.92% accuracy. They built a web application with a user interface to input applicant data and receive predictions. The web app includes additional features like an interest rate calculator and finance news section. The project aims to streamline the loan approval process and reduce human workload and errors.
This document discusses various uses of AI in banking, including:
1) Know Your Customer/Client (KYC) and fraud detection using machine learning to analyze transactions and communications.
2) Anomaly detection using time series analysis to flag suspicious transaction patterns in real-time.
3) Customer churn prediction analyzing complex customer behavior data to identify at-risk customers.
Credit Card Fraud Detection Using ML In DatabricksDatabricks
In the Credit Card Companies, illegitimate credit card usage is a serious problem which results in a need to accurately detect fraudulent transactions vs non-fraudulent transactions. All organizations can be hugely impacted by fraud and fraudulent activities, especially those in financial services. The threat can originate from internal or external, but the effects can be devastating – including loss of consumer confidence, incarceration for those involved, even up to downfall of a corporation. Despite regular fraud prevention measures, these are constantly being put to the test in an attempt to beat the system.
Fraud detection is a task of predicting whether a card has been used by the cardholder. One of the methods to recognize fraud card usage is to leverage Machine Learning (ML) models. In order to more dynamically detect fraudulent transactions, one can train ML models on a set of dataset including credit card transaction information as well as card and demographic information of the owner of the account. This will be our goal of the project while leveraging Databricks.
Consumers are looking for more than just banking and machine learning helps banks deliver that.
Machine learning contributes to areas such as credit decisions, risk management, personalized customer experiences, fraud detection, automation and much more.
This PDF will address the following points:
1. An overview of the banking sector and its importance in the economy
2. The top 5 banks in the US benefiting from the power of machine learning
3. The areas in banking where Machine Learning is applied
In this new Accenture Finance & Risk presentation we explore machine learning as a solution to some of the most important challenges faced by the banking sector today. To learn more, read our blog on Machine Learning in Banking: https://accntu.re/2oTVJiX
Artificial intelligence applications are increasingly being used in the financial sector. Chatbots can help reduce costs by automating some customer service tasks, while machine learning algorithms can help make know-your-customer processes more efficient by identifying patterns in transaction data. Artificial intelligence may also allow for more accurate foreign exchange price predictions and personalized robo-advisor services. These applications demonstrate how artificial intelligence is disrupting traditional financial services.
Machine Learning (ML) for Fraud Detection.
- fraud is a big problem (big data, big cost)
- ML on bigger data produces better results
- Industry standard today (for detecting fraud)
- How to improve fraud detection!
This document describes a student project to build a machine learning model to predict loan eligibility. A group of 4 students created a model using random forest classification that achieved 77.92% accuracy. They built a web application with a user interface to input applicant data and receive predictions. The web app includes additional features like an interest rate calculator and finance news section. The project aims to streamline the loan approval process and reduce human workload and errors.
This document discusses various uses of AI in banking, including:
1) Know Your Customer/Client (KYC) and fraud detection using machine learning to analyze transactions and communications.
2) Anomaly detection using time series analysis to flag suspicious transaction patterns in real-time.
3) Customer churn prediction analyzing complex customer behavior data to identify at-risk customers.
Credit Card Fraud Detection Using ML In DatabricksDatabricks
In the Credit Card Companies, illegitimate credit card usage is a serious problem which results in a need to accurately detect fraudulent transactions vs non-fraudulent transactions. All organizations can be hugely impacted by fraud and fraudulent activities, especially those in financial services. The threat can originate from internal or external, but the effects can be devastating – including loss of consumer confidence, incarceration for those involved, even up to downfall of a corporation. Despite regular fraud prevention measures, these are constantly being put to the test in an attempt to beat the system.
Fraud detection is a task of predicting whether a card has been used by the cardholder. One of the methods to recognize fraud card usage is to leverage Machine Learning (ML) models. In order to more dynamically detect fraudulent transactions, one can train ML models on a set of dataset including credit card transaction information as well as card and demographic information of the owner of the account. This will be our goal of the project while leveraging Databricks.
Consumers are looking for more than just banking and machine learning helps banks deliver that.
Machine learning contributes to areas such as credit decisions, risk management, personalized customer experiences, fraud detection, automation and much more.
This PDF will address the following points:
1. An overview of the banking sector and its importance in the economy
2. The top 5 banks in the US benefiting from the power of machine learning
3. The areas in banking where Machine Learning is applied
Loan approval prediction based on machine learning approachEslam Nader
This document discusses using machine learning models to predict loan approvals. It introduces the motivation, problem statement, and objectives of building a loan prediction system. The document describes the dataset used, which contains information about previous loan applicants. It then explains three machine learning models tested for the predictions: decision tree classifier, logistic regression, and naive Bayesian classifier. The document concludes by reporting the accuracy scores from experimenting with each model, with decision tree performing best.
This document discusses using machine learning for fraud detection. It outlines how machine learning can provide a scalable, adaptable solution to identify fraud. The machine learning pipeline involves gathering data from user accounts, selecting a model like CatBoost that handles categorical data well, training and evaluating the model, and deploying the trained model to classify new users as fraudulent or not. The goal is to balance avoiding false positives and false negatives when identifying fraud.
Artificial Intelligence for Banking Fraud PreventionJérôme Kehrli
Artificial Intelligence at NetGuardians:
"From skepticism to large scale adoption towards fraud prevention"
Slides of my speech at the EPFL / EMBA Innovation Leader 2018 event.
Artificial Intelligence: a driver of innovation in the Banking Sector - The Italian case
Marco Rotoloni (Head of the research team on banking operations, ABI Lab)
Artificial intelligence and semantic computing can assist the financial services industry in several ways:
- Machine learning and neural networks can analyze large amounts of data to detect patterns and make predictions about customer behavior, risks, and opportunities. This includes predictive analytics, risk analysis, and personalized recommendations.
- Natural language processing allows customers to interact with services using human language across different channels. It also enables analysis of unstructured data like text to gain insights.
- Semantic computing uses ontologies and semantic queries to understand relationships and context in data from various sources, helping to integrate information more easily.
- Together these tools could help with tasks like marketing and pricing optimization, fraud detection, faster claims processing, and more personalized
The document provides an overview of research conducted by the London School of Economics on behalf of EY to investigate the use of artificial intelligence and machine learning in the financial services sector. It examines one use case for insurance, banking/capital markets, and wealth/asset management. The key findings are:
- Applied AI, mainly machine learning, is currently used across industries to solve isolated problems. Partnerships between large firms and startups are common.
- Prominent use cases illustrated trends in each sector, such as fraud detection in banking, predictive analytics in wealth management, and Internet of Things/home security applications in insurance.
- Both short and long term impacts are expected as machine learning capabilities advance, including changes
The document discusses data warehousing, knowledge discovery in databases (KDD), and data mining. It defines a data warehouse as a subject-oriented collection of integrated and non-volatile data used to support management decision making. Data mining is extracting knowledge from large amounts of data and has applications in business transactions, ecommerce, healthcare, and more. Specifically for banking, data mining can be used for marketing, risk management, and customer acquisition/retention by identifying patterns in large customer data sets.
The document discusses the use of artificial intelligence and machine learning in the financial industry. It covers emerging trends like increased regulations, growth of digital technologies, and the emergence of AI/ML. It also discusses key concepts like big data, different types of machine learning (supervised, unsupervised, reinforcement, deep learning), and applications in areas like portfolio management, algorithmic trading, fraud detection, and chatbots. The future of AI in finance is seen as promising with potential for more widespread use of these technologies across various business problems in finance and other industries.
Artificial Intelligence and Digital Banking - What about fraud prevention ?Jérôme Kehrli
Artificial intelligence for banking fraud prevention.
A presentation on how it takes its root in the digitalisation ways and how it impacts customer experience.
An Introduction to Digital Credit: Resources to Plan a DeploymentCGAP
This is a workshop/course offering guidance in developing new digital credit products. This content is designed for a broad audience of banks, mobile operators, lenders, and fintech firms. It may also be of interest to regulators, policy makers and investors/donors.
With any comments or to request more materials (including the financial model [Excel] or original PPT presentation with detailed presenter notes), please write to cgap [@] worldbank.org.
Adaptive Machine Learning for Credit Card Fraud DetectionAndrea Dal Pozzolo
This document discusses machine learning techniques for credit card fraud detection. It addresses challenges like concept drift, imbalanced data, and limited supervised data. The author proposes contributions in learning from imbalanced and evolving data streams, a prototype fraud detection system using all supervised information, and a software package/dataset. Methods discussed include resampling techniques, concept drift handling, and a "racing" algorithm to efficiently select the best strategy for unbalanced classification on a given dataset. Evaluation measures the ability to accurately rank transactions by fraud risk.
FinTech, AI, Machine Learning in FinanceSanjiv Das
Alexa, Siri, Cortana, Google Assistant
- Vision: Amazon Rekognition, Google Cloud Vision
- Natural Language: IBM Watson, Microsoft LUIS
- Recommendation: Amazon Personalize
- Translation: Google Translate, Microsoft Translator
- Speech: Amazon Polly, Google Cloud Speech
- Conversational AI: Anthropic, Anthropic, Anthropic
- Custom AI Solutions: Google Cloud AI, Microsoft Azure ML
- Low-Code AI: Anthropic, DataRobot, H2O.ai
- Edge AI: AWS Greengrass, Google Edge TPU
- AI Chips: Google TPU, Intel Nervana, Nvidia GPU
This document analyzes various methods for credit card fraud detection. It discusses techniques like Dempster-Shafer theory, BLAST-SSAHA hybridization, hidden Markov models, evolutionary-fuzzy systems, and using Bayesian and neural networks. The document also compares the different fraud detection systems based on parameters like accuracy, method, true positive rate, false positive rate, and training data needed. In conclusion, the document states that efficient fraud detection is required, and techniques like fuzzy Darwinian systems and neural networks show good accuracy, while hidden Markov models have a low fraud detection rate.
This document summarizes a project to reduce fraudulent card transactions for a US national bank. An ensemble technique using logistic regression and K-nearest neighbors was developed to classify transactions as fraudulent or legitimate in real time. The project was estimated to reduce fraudulent losses by $16-18 million while costing $4.2 million to develop. Testing on 1 year of transaction data accurately classified transactions and reduced fraudulent cases by 80-90%, saving the bank $16 million.
Credit card fraud detection using machine learning Algorithmsankit panigrahy
This document discusses credit card fraud detection using machine learning techniques. It compares the performance of naïve bayes, k-nearest neighbor, and logistic regression classifiers on a credit card transactions dataset. The dataset contains over 284,000 transactions with 0.172% fraudulent cases, making the data highly imbalanced. Different resampling techniques are used to address this imbalance. The performance of the classifiers is evaluated based on various metrics like accuracy, sensitivity, specificity, and F1 score. The results show that kNN performs best for most metrics except accuracy on a specific class distribution, while naïve bayes and logistic regression also achieve good performance.
This document provides an agenda for a presentation on AI and machine learning for financial professionals. The presentation will be given by Sri Krishnamurthy, founder and CEO of QuantUniversity. The agenda includes introductions of the speaker and an overview of QuantUniversity. It then covers key trends in AI/ML, the basics of machine learning in 30 minutes, building a machine learning application in 10 steps, and case studies of how AI/ML are used in finance from companies like Bank of America, Ravenpack, and Northfield.
Credit Card Fraudulent Transaction Detection Research PaperGarvit Burad
Credit Card Fraudulent Transaction Detection Research Paper using Machine Learning technologies like Logistic Regression, Random Forrest, Feature Engineering and various techniques to deal with highly skewed dataset
Rise of Artificial Intelligence in InsuranceAnandSRao1962
The document discusses the rise of artificial intelligence in the insurance industry. It covers how AI is being applied to key areas like underwriting, loss management, claims, and fraud detection. It also discusses implications for insurers, including developing an AI strategy and building internal AI capabilities. AI technologies like machine learning, deep learning, and robotic process automation are helping insurers automate processes, gain insights from data, and enhance customer experience.
This document discusses anomaly and fraud detection using machine learning. It outlines different applications of anomaly detection such as cybersecurity and fraud detection. It compares supervised versus unsupervised learning approaches for financial sector applications. Specific algorithms discussed for unsupervised anomaly detection include isolation forest, DBSCAN, HDBSCAN, local outlier factor, and Gaussian mixture models.
This presentation presents an overview of the challenges and opportunities of generative artificial intelligence in Web3. It includes a brief research history of generative AI as well as some of its immediate applications in Web3.
#IBMInsight session presentation "Mitigate Risk, Combat Fraud and Financial Crimes"
The Issue of fraud, challenges, fighting fraud as an enterprise endeavor, IBM Smarter counter fraud framework and IBM Counter Fraud business services
More at ibm.biz/BdEPRH
Fortify Your Enterprise with IBM Smarter Counter-Fraud SolutionsPerficient, Inc.
Organizations lose an estimated five percent of annual revenues to fraud, totaling nearly $1 trillion in the U.S. alone. Cyber criminals are more organized and better equipped than ever, and continue to evolve their strategies in order to undermine even the strongest protections.
We continue to hear about major security breaches across all industries, but what is being done to fix the problem? There must be a tight interlock between risk, security, fraud and financial crimes management. Current solutions are proving inadequate as point solutions and a corporate silo mentality directly contribute to the risk of fraudulent activities going undetected.
Our webinar covered:
-How IBM’s Smarter Counter Fraud initiative can help public and private organizations prevent, identify and investigate fraudulent activities
-Real-world use cases including how one financial institution stopped $1M in fraud in the first week after implementing a counter-fraud solution
-Perficient’s multi-tiered approach to help guide successful business outcomes
It’s time to stop the bad guys with IBM Smarter Counter Fraud and Perficient – learn how now!
Loan approval prediction based on machine learning approachEslam Nader
This document discusses using machine learning models to predict loan approvals. It introduces the motivation, problem statement, and objectives of building a loan prediction system. The document describes the dataset used, which contains information about previous loan applicants. It then explains three machine learning models tested for the predictions: decision tree classifier, logistic regression, and naive Bayesian classifier. The document concludes by reporting the accuracy scores from experimenting with each model, with decision tree performing best.
This document discusses using machine learning for fraud detection. It outlines how machine learning can provide a scalable, adaptable solution to identify fraud. The machine learning pipeline involves gathering data from user accounts, selecting a model like CatBoost that handles categorical data well, training and evaluating the model, and deploying the trained model to classify new users as fraudulent or not. The goal is to balance avoiding false positives and false negatives when identifying fraud.
Artificial Intelligence for Banking Fraud PreventionJérôme Kehrli
Artificial Intelligence at NetGuardians:
"From skepticism to large scale adoption towards fraud prevention"
Slides of my speech at the EPFL / EMBA Innovation Leader 2018 event.
Artificial Intelligence: a driver of innovation in the Banking Sector - The Italian case
Marco Rotoloni (Head of the research team on banking operations, ABI Lab)
Artificial intelligence and semantic computing can assist the financial services industry in several ways:
- Machine learning and neural networks can analyze large amounts of data to detect patterns and make predictions about customer behavior, risks, and opportunities. This includes predictive analytics, risk analysis, and personalized recommendations.
- Natural language processing allows customers to interact with services using human language across different channels. It also enables analysis of unstructured data like text to gain insights.
- Semantic computing uses ontologies and semantic queries to understand relationships and context in data from various sources, helping to integrate information more easily.
- Together these tools could help with tasks like marketing and pricing optimization, fraud detection, faster claims processing, and more personalized
The document provides an overview of research conducted by the London School of Economics on behalf of EY to investigate the use of artificial intelligence and machine learning in the financial services sector. It examines one use case for insurance, banking/capital markets, and wealth/asset management. The key findings are:
- Applied AI, mainly machine learning, is currently used across industries to solve isolated problems. Partnerships between large firms and startups are common.
- Prominent use cases illustrated trends in each sector, such as fraud detection in banking, predictive analytics in wealth management, and Internet of Things/home security applications in insurance.
- Both short and long term impacts are expected as machine learning capabilities advance, including changes
The document discusses data warehousing, knowledge discovery in databases (KDD), and data mining. It defines a data warehouse as a subject-oriented collection of integrated and non-volatile data used to support management decision making. Data mining is extracting knowledge from large amounts of data and has applications in business transactions, ecommerce, healthcare, and more. Specifically for banking, data mining can be used for marketing, risk management, and customer acquisition/retention by identifying patterns in large customer data sets.
The document discusses the use of artificial intelligence and machine learning in the financial industry. It covers emerging trends like increased regulations, growth of digital technologies, and the emergence of AI/ML. It also discusses key concepts like big data, different types of machine learning (supervised, unsupervised, reinforcement, deep learning), and applications in areas like portfolio management, algorithmic trading, fraud detection, and chatbots. The future of AI in finance is seen as promising with potential for more widespread use of these technologies across various business problems in finance and other industries.
Artificial Intelligence and Digital Banking - What about fraud prevention ?Jérôme Kehrli
Artificial intelligence for banking fraud prevention.
A presentation on how it takes its root in the digitalisation ways and how it impacts customer experience.
An Introduction to Digital Credit: Resources to Plan a DeploymentCGAP
This is a workshop/course offering guidance in developing new digital credit products. This content is designed for a broad audience of banks, mobile operators, lenders, and fintech firms. It may also be of interest to regulators, policy makers and investors/donors.
With any comments or to request more materials (including the financial model [Excel] or original PPT presentation with detailed presenter notes), please write to cgap [@] worldbank.org.
Adaptive Machine Learning for Credit Card Fraud DetectionAndrea Dal Pozzolo
This document discusses machine learning techniques for credit card fraud detection. It addresses challenges like concept drift, imbalanced data, and limited supervised data. The author proposes contributions in learning from imbalanced and evolving data streams, a prototype fraud detection system using all supervised information, and a software package/dataset. Methods discussed include resampling techniques, concept drift handling, and a "racing" algorithm to efficiently select the best strategy for unbalanced classification on a given dataset. Evaluation measures the ability to accurately rank transactions by fraud risk.
FinTech, AI, Machine Learning in FinanceSanjiv Das
Alexa, Siri, Cortana, Google Assistant
- Vision: Amazon Rekognition, Google Cloud Vision
- Natural Language: IBM Watson, Microsoft LUIS
- Recommendation: Amazon Personalize
- Translation: Google Translate, Microsoft Translator
- Speech: Amazon Polly, Google Cloud Speech
- Conversational AI: Anthropic, Anthropic, Anthropic
- Custom AI Solutions: Google Cloud AI, Microsoft Azure ML
- Low-Code AI: Anthropic, DataRobot, H2O.ai
- Edge AI: AWS Greengrass, Google Edge TPU
- AI Chips: Google TPU, Intel Nervana, Nvidia GPU
This document analyzes various methods for credit card fraud detection. It discusses techniques like Dempster-Shafer theory, BLAST-SSAHA hybridization, hidden Markov models, evolutionary-fuzzy systems, and using Bayesian and neural networks. The document also compares the different fraud detection systems based on parameters like accuracy, method, true positive rate, false positive rate, and training data needed. In conclusion, the document states that efficient fraud detection is required, and techniques like fuzzy Darwinian systems and neural networks show good accuracy, while hidden Markov models have a low fraud detection rate.
This document summarizes a project to reduce fraudulent card transactions for a US national bank. An ensemble technique using logistic regression and K-nearest neighbors was developed to classify transactions as fraudulent or legitimate in real time. The project was estimated to reduce fraudulent losses by $16-18 million while costing $4.2 million to develop. Testing on 1 year of transaction data accurately classified transactions and reduced fraudulent cases by 80-90%, saving the bank $16 million.
Credit card fraud detection using machine learning Algorithmsankit panigrahy
This document discusses credit card fraud detection using machine learning techniques. It compares the performance of naïve bayes, k-nearest neighbor, and logistic regression classifiers on a credit card transactions dataset. The dataset contains over 284,000 transactions with 0.172% fraudulent cases, making the data highly imbalanced. Different resampling techniques are used to address this imbalance. The performance of the classifiers is evaluated based on various metrics like accuracy, sensitivity, specificity, and F1 score. The results show that kNN performs best for most metrics except accuracy on a specific class distribution, while naïve bayes and logistic regression also achieve good performance.
This document provides an agenda for a presentation on AI and machine learning for financial professionals. The presentation will be given by Sri Krishnamurthy, founder and CEO of QuantUniversity. The agenda includes introductions of the speaker and an overview of QuantUniversity. It then covers key trends in AI/ML, the basics of machine learning in 30 minutes, building a machine learning application in 10 steps, and case studies of how AI/ML are used in finance from companies like Bank of America, Ravenpack, and Northfield.
Credit Card Fraudulent Transaction Detection Research PaperGarvit Burad
Credit Card Fraudulent Transaction Detection Research Paper using Machine Learning technologies like Logistic Regression, Random Forrest, Feature Engineering and various techniques to deal with highly skewed dataset
Rise of Artificial Intelligence in InsuranceAnandSRao1962
The document discusses the rise of artificial intelligence in the insurance industry. It covers how AI is being applied to key areas like underwriting, loss management, claims, and fraud detection. It also discusses implications for insurers, including developing an AI strategy and building internal AI capabilities. AI technologies like machine learning, deep learning, and robotic process automation are helping insurers automate processes, gain insights from data, and enhance customer experience.
This document discusses anomaly and fraud detection using machine learning. It outlines different applications of anomaly detection such as cybersecurity and fraud detection. It compares supervised versus unsupervised learning approaches for financial sector applications. Specific algorithms discussed for unsupervised anomaly detection include isolation forest, DBSCAN, HDBSCAN, local outlier factor, and Gaussian mixture models.
This presentation presents an overview of the challenges and opportunities of generative artificial intelligence in Web3. It includes a brief research history of generative AI as well as some of its immediate applications in Web3.
#IBMInsight session presentation "Mitigate Risk, Combat Fraud and Financial Crimes"
The Issue of fraud, challenges, fighting fraud as an enterprise endeavor, IBM Smarter counter fraud framework and IBM Counter Fraud business services
More at ibm.biz/BdEPRH
Fortify Your Enterprise with IBM Smarter Counter-Fraud SolutionsPerficient, Inc.
Organizations lose an estimated five percent of annual revenues to fraud, totaling nearly $1 trillion in the U.S. alone. Cyber criminals are more organized and better equipped than ever, and continue to evolve their strategies in order to undermine even the strongest protections.
We continue to hear about major security breaches across all industries, but what is being done to fix the problem? There must be a tight interlock between risk, security, fraud and financial crimes management. Current solutions are proving inadequate as point solutions and a corporate silo mentality directly contribute to the risk of fraudulent activities going undetected.
Our webinar covered:
-How IBM’s Smarter Counter Fraud initiative can help public and private organizations prevent, identify and investigate fraudulent activities
-Real-world use cases including how one financial institution stopped $1M in fraud in the first week after implementing a counter-fraud solution
-Perficient’s multi-tiered approach to help guide successful business outcomes
It’s time to stop the bad guys with IBM Smarter Counter Fraud and Perficient – learn how now!
1) The document discusses the application of artificial intelligence in finance fraud detection. It outlines key points such as different AI applications in finance, the impact of AI, and methodology.
2) AI systems use machine learning algorithms to analyze financial data and identify patterns that indicate fraudulent activity in real-time. This helps reduce fraud and financial losses.
3) The future of AI in finance fraud detection is promising, with potential applications including advanced machine learning, natural language processing, biometric authentication, and more automated risk management and investment processes.
Learn how IBM Smarter Analytics Solution for insurance helps Detect and prevent insurance claims fraud, waste and abuse. For more information on IBM Systems, visit http://ibm.co/RKEeMO.
Visit the official Scribd Channel of IBM India Smarter Computing at http://bit.ly/VwO86R to get access to more documents.
This document discusses how machine learning can help detect fraud. It explains that machine learning models are trained on historical transaction data to learn patterns and detect anomalies. Common machine learning algorithms used for fraud detection include logistic regression, decision trees, random forests, and neural networks. While machine learning is effective for fraud detection, it also has some limitations such as a lack of interpretability and needing sufficient data to identify patterns. An example is provided of a global bank that implemented a machine learning solution to reduce check fraud losses by speeding up verification.
The document discusses the use of artificial intelligence in finance fraud detection. It begins with an introduction on AI and its increasing use in the finance industry. It then discusses different applications of AI in finance fraud detection such as real-time transaction monitoring, pattern recognition, and machine learning. The document also covers the impact of AI on fraud detection through improved accuracy, efficiency and effectiveness. Finally, it discusses future scopes of AI including advanced machine learning algorithms and natural language processing.
Enterprise Fraud Management: How Banks Need to AdaptCapgemini
Fraud prevention is becoming one of the biggest areas of concern for the financial services industry. But first generation Fraud Management systems are falling short. By moving towards more enterprise approach to fraud management, financial institutions can combat the increasingly treacherous fraud and cyber crime landscape while reaping numerous benefits for the organization.
Ten Commandments for Tackling Fraud: The Role of Big Data and Predictive Anal...CA Technologies
Accurate enterprise-wide data combined with data-driven fraud analytics can have a transformational effect on banking and related industries. This presentation provides tips and insights on using technologies like neural network predictive modeling, user behavior-based pattern recognition and statistical big data analytics to reduce the risk of fraudulent activities in the enterprise.
For more information on CA Security solutions, please visit: http://bit.ly/10WHYDm
with great enthusiasm Insights Success has
shortlisted The 10 Most Trusted Fraud Detection
Solution Providers, 2019, who are working round the
clock to help is clients detect fraud, faster!
Ibm ofa ottawa_analytics_in_gov _campbell_robertsondawnrk
Opportunity for Analytics Ottawa event. Presentation by Campbell Robertson, Analytics in Government. Results based outcomes with IBM Predictive Analysis for Cost Avoidance and Beyond.
Ibm ofa ottawa_analytics_in_gov _campbell_robertsondawnrk
IBM Opportunity for Analytics Event, Ottawa, Analytics in Government, Results-based outcomes with IBM Predictive Analysis for Cost Avoidance and Beyond, presented by Campbell Robertson.
Online Transaction Fraud Detection using Hidden Markov Model & Behavior AnalysisCSCJournals
Card payment are mostly preferred by many for transactions instead of cash. Due to its convenience, it is the most accepted payment method for offline as well as online purchases, irrespective of region or country the purchase is made. Currently, cards are used for everyday activities, such as online shopping, bill pays, subscriptions, etc. Consequently, there are more chances of fraudulent transactions. Online transactions are the prime target as it does not require real card, only card details are enough and can be stored digitally. The current system detects the fraud transaction after the transaction is completed. Proposed system in this paper, uses Hidden Markov Model (HMM), which is one of the statistical stochastic models used to model randomly changing systems. Using Hidden Markov Model, a fraud transaction can be detected during the time of transaction itself and after 3 attempts of verification card can blocked at the same time. Behavior Analysis (BA) helps to understand the spending habits of cardholder. Hidden Markov Model helps to acquire high-level fraud analysis with a low false alarm ratio.
Towards the Next Generation Financial Crimes Platform - How Data, Analytics, ...Molly Alexander
Towards the Next Generation Financial Crimes Platform - How Data, Analytics, & ML Are Transforming the Fight Against Fraud, AML & Cybersecurity -Nadeem Asghar
FRAUD DETECTION IN CREDIT CARD TRANSACTIONSIRJET Journal
This document summarizes a research paper on detecting credit card fraud using machine learning algorithms. It begins by introducing the challenges of credit card fraud detection and how traditional methods are insufficient. Then it discusses how machine learning algorithms can be applied to transaction data to identify complex fraud patterns in real-time. The document outlines the methodology, including data collection, preprocessing, feature extraction, model selection and training, and model evaluation. Finally, it presents the results and performance of logistic regression, support vector machines, and random forest algorithms on the fraud detection task and concludes that machine learning is a promising approach.
5 AI Solutions Every Chief Risk Officer NeedsAlisa Karybina
For the risk manager, AI means greater efficiency, lower costs, and less risk. There are many potential applications of AI when it comes to managing risk in banking, but this report will focus on five key solutions with huge potential ROI that every chief risk officer (CRO) can begin building immediately. Representing foundational capabilities for risk management, these five solutions have the potential to substantially impact a bank’s financial results, and an automated machine learning platform represents the most efficient and effective method of delivering on the promise of these AI use cases.
IRJET- Survey on Credit Card Fraud DetectionIRJET Journal
This document discusses various techniques for credit card fraud detection. It begins with an introduction to fraud detection and challenges in detecting credit card fraud. It then summarizes 6 research papers on different fraud detection techniques, including cost-sensitive decision trees, hidden Markov models, self-organizing maps, cortical learning algorithms, a fusion approach using Dempster-Shafer theory and Bayesian learning, and modified Fisher discriminant analysis. The document concludes that while various machine learning techniques have been applied to fraud detection, loss from credit card fraud continues to increase due to evolving fraud tactics, and improved dynamic systems are still needed.
Similar to Ai and machine learning help detect, predict and prevent fraud - IBM Watson Data Science Meetup (20)
Filip Panjevic is a Co-Founder and CTO at ydrive.ai - startup dealing with self-driving cars, and one of the founders of Petnica Machine Learning School.
Filip's talk will focus on the story of Petnica School, how did it start, what has changed since the beginning, how the concept of school looks right now and why is that concept good for making new data scientists. This talk will be perfect for people who consider starting their careers in the data science field!
The talk will be a broad overview and thoughts about building one of the biggest data science communities in India. I will talk about how an ecosystem is created and value delivered to each stakeholder. I will be sharing my experience of building MachineHack and AIMinds and other platforms. One of the core agendas of the talk will be how these platforms have enabled a unique data science education and learning experience in India. The platforms built help students and engineers to imagine and work towards a career in data science.
In Drazen talk, you will get a chance to listen to how Data Science Master 4.0 on Belgrade University was created, and what are the benefits of the program.
PwC's recently released Responsible AI Diagnostic surveyed around 250 senior business executives from May to June 2019. The survey says that 84% of CEOs agree that AI-based decisions need to be explainable in order to be trusted. In the past few years, Deep learning has shown remarkable results in various applications, which makes it one of the first choices for many AI use cases. However, deep learning models are hard to explain, and since the majority of CEOs expect AI solutions to be explainable, deep learning has a serious challenge. Daniel Kahneman, in his book thinking fast and slow, presented two different systems the human brain uses to form thoughts and decisions: System 1: fast, intuitive and hard to explain System 2: slow, conscious and easy to explain In this talk I will present: A) PwC Responsible AI Survey B) A proposed deep learning framework that mimics the two systems of thinking C) The recent advances in the neural symbolic learning field.
Challenges in building a churn prediction model in different industries, presented by Jelena Pekez from Comtrade System Integration. Talk is focused on real-life use-case experience.
This document discusses using business intelligence (BI) to improve risk management at a bank. It provides three key ways BI can create value: protecting revenue, improving risk assessments, and reducing operational costs. Specific use cases are described, including early warning systems, behavioral detection systems, and modern BI platforms that combine data aggregation, analytics, and infrastructure for faster insights. The presentation outlines a proof of concept and roadmap for implementing a modern BI system at the bank to enable self-service analytics, alerts, automated data delivery, and collaboration across the organization. Dashboards and data insights are shown as examples of the types of risk analyses and reporting that will be possible with a new modern BI platform.
The talk will have 3 parts. The overview of the practical applications of the AI and ML in the FinTech industry with a short explanation of the PSD2 directive and the disruption is caused. Application of the AI/ML from the perspective of the end-user, personal financial health, financial coach, etc. The overview of the architecture, technologies, and frameworks used with practical examples from the Zuper company.
We present a recommender system for personalized financial advice, which we designed for a large Swiss private bank. The final recommendations produced by the system were delivered to the end clients through a mobile banking platform. The recommender system is based on a collaborative filtering technique and can work with changing asset features, operate with implicit ratings and react to explicit feedback that clients can give using the mobile app. Moreover, we developed and implemented an approach to provide an explanation for each recommendation in the form “As you bought A, you might like B".
This talk shall focus on making real-time pipelines using cutting edge Big Data technologies and applying ML on gathered data. The first part of the presentation shall cover importance and necessity for streaming data processing. In addition, tools that could be used in order to build a streaming pipeline shall be proposed. The second part of this talk shall focus on making machine learning models in customer support. There shall be introduced success stories covering the need for more efficient customer support, problem resolution and gained benefits.
Presentation of the first complete AI investment platform. It is based on most innovative AI methods: most advanced neural networks (ResNet/DenseNet, LSTM, GAN autoencoders) and reinforcement learning for risk control and position sizing using Alpha Zero approach. It shows how the complex AI system which covers both supervised and reinforcement learning could be successfully used to investment portfolio optimization in real-time. The architecture of the platform and used algorithms will be presented together with the workflow of machine learning. Also, the real demo of the platform will be shown.
A lot of companies make the mistake of thinking that just hiring Data Scientists will lead to increased revenue or increased profit. For a company’s investment in Data Science to be successful the Data Scientists need to work on the right problems, with the right people, and with the right tools. In this presentation, I will talk about the lessons I have learned, and mistakes made in applying Data Science in commercial settings over the last 10 years. I will highlight what processes can increase the chances of Data Science investment being successful.
The talk would be focusing on reasons and method for creating models which maximize sales price Gross Margin but still has high confidentiality that quote would be accepted by the client. Price changes are dynamic things that are impacted by many different elements like cost of input material, labor cost, transportation cost, scrap material due to different ordered quantities, etc. Besides input cost segments, output price is also impacted by different marketing campaigns (own and others), seasonality, past and future customer behavior as well as the behavior of the product we are selling.
Data is now a valuable asset for businesses, as companies that effectively use data are outperforming their peers by moving further ahead faster and more cost-effectively. However, some businesses remain indifferent to the value of data, failing to take charge of this new gold, while customer expectations have never been higher. Coeus claims to add fuel to businesses by providing higher conversion rates through effective use of data.
In the past few years, many businesses started do understand the potential of real-time data analytics. And many of those invested time, energy and finances to make it happen, with weaker outcomes than expected. Reasons are few for this: too ambitious plans by leadership regarding leveraging data, not enough discipline defining goals and MVP for initial use cases, a plethora of tools and vendors available who claim that can solve all the problems, etc. So, how can we get the most value with reasonable costs out of fast (real-time) data? We will try to answer this question and give actionable advice.
This document discusses the design of a personalized 3A health monitoring system using sensor networks. It begins with an overview of current challenges in healthcare like an aging population and increasing costs. It then describes the proposed system which would use sensors, edge computing, blockchain and other technologies to provide continuous remote health monitoring anywhere and anytime. Key aspects of the system include a heart rate monitoring solution, data exchange centers, smart health homes and eHealth labs. The system aims to address issues like data ownership and security while providing personalized care. It concludes by discussing next steps to test and implement the continuous monitoring system.
This document discusses improving data quality through product similarity search. It describes leveraging multiple data sources like product names, descriptions, prices and attributes to calculate similarity between products. Different techniques are used depending on the data type, such as text similarity for names/descriptions, numerical similarity for prices and variant counts, and mixed similarity for attributes. Attributes require special handling due to different data types within. The document outlines challenges in comparing incompatible datasets and noisy data. It proposes a solution using an API that can customize similarity based on use cases and data specificities.
Uroš Valant has almost 20 years of experience in planning, managing and delivering of various IT projects. He has the best and richest experience in the field of business analytics, project planning and implementation, database design and the management of development teams. In the last years, his focus is the field of predictive analytics, machine learning and applying the AI solution to a practical use in different field of work.
In his talk he will present to us interactive case study of the image recognition use and AI assisted design techniques in the textile industry.
The presentation will start as an engaging lecture where I will present the motivation behind the project based on my academic research (my Oxford PhD among others). I will tell the audience just how rampant corruption is in local governance and why is it so persistent. Then I will present our remedy: full budget transparency. I will show them our search engine and how it works, and will call the participants to download the APIs and play with the data themselves.
The talk will be divided into two parts. The first one is about geospatial open data and several Copernicus services where those data can be downloaded. The second one is about Forest and Climate project, as an example of geospatial analysis. The aim of the project was to identify the most suitable area for afforestation in Serbia by using satellite and Earth observation data. The results can be found at https://sumeiklima.org/.
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
Natural Language Processing (NLP), RAG and its applications .pptxfkyes25
1. In the realm of Natural Language Processing (NLP), knowledge-intensive tasks such as question answering, fact verification, and open-domain dialogue generation require the integration of vast and up-to-date information. Traditional neural models, though powerful, struggle with encoding all necessary knowledge within their parameters, leading to limitations in generalization and scalability. The paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" introduces RAG (Retrieval-Augmented Generation), a novel framework that synergizes retrieval mechanisms with generative models, enhancing performance by dynamically incorporating external knowledge during inference.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Ai and machine learning help detect, predict and prevent fraud - IBM Watson Data Science Meetup
1. AI and machine learning
help detect, predict and
prevent fraud
Nina Lozo
Data & AI Technical Professional
IBM South East Europe,
nina.lozo@rs.ibm.com
IBM Watson Data Science Meetup
8. Data Science in Fraud
Detection
8
A data science platform is
complimentary to other fraud
detection systems
• React: Predictive models can quickly
determine changing patterns in fraud
and react to them in real time
• Improve: Data science can help
derive new fraud detection rules,
which can be used to improve the
business process
• Achieve more: Data science can
increase the rate of fraud detection
15. Advanced analytics techniques can dramatically
improve the effectiveness and efficiency of fraud
management…
Where once fraud was detected by risk functions flagging
suspect transactions for manual review, firms can now use
neural networks based on unsupervised and supervised
architectures to monitor dubious activities.
– McKinsey & Company
Fraud management: Recovering value through next-generation solutions
16. 16
AI drives real business value in fraud prevention
Faster screening
updates
Enhanced screening
models
Enhanced accuracy
of fraud profiling
Enhanced identity
verification
Centralization of
fraud processes
Enhanced fraud
analytics tools
Lower cost of fraud
infrastructure
Reduced fraud false
positive rates
Improved
investigations
process
Facilitate
investigation case
management
Automated fraud
reporting
Comply with voluntary
and mandated
regulations while
differentiating
competitive position
Reduced costs of
payment fraud
losses
Reduced costs of
fraud screening &
monitoring
Reduced cost of
fraud investigations
Reduced cost of
compliance
reporting of fraud
17. AI enables you to predict likelihood of fraud and proactively act upon
insight to drive better prevention
Capture
Data Collection delivers an accurate view
of customer attitudes and opinions
Predict Act
Predictive capabilities bring repeatability
to ongoing decision making, and drive
confidence in your results and decisions
Unique deployment technologies and
methodologies maximize the impact of
analytics in your operation
…
…
Data
Collection
Deployment
TechnologiesPlatform
Deep
Learning
Detect Predict Analyze
Data
Mining
Machine
Learning
18. Easily & seamlessly
move from sandbox
to production
Connect data science
models with real-time
data
Deploy predictive
models into business
process
Create more “citizen
data scientists” with
visual modeling
Train advanced ML
models without data
science degree
Make fraud detection
easier
Empower data
scientists to get
ahead of fraudsters
Enable deep learning
and neural networks
Get latest models and
frameworks in fraud
prediction
Leverage more
unstructured data like
text and images
Easy tooling to train
models in a few clicks
Leverage pre-trained
APIs to jump start
development process
Move from fraud
detection to fraud
prediction
Upskill the team
to do more data
science
Stay ahead of
fraudsters with
latest ML models
Make faster and
more accurate
prediction
Strategies to stay ahead
19. 19
“With the data mining
system, we generated
productivity savings of
nearly 80 percent.”
Francisco Ruiz
Head of Compliance,
Bancolombia
Solution
Deployed predictive data-modeling software that helped it more easily and
quickly detect transactions that were part of potential money-laundering
operations
Solution prevents, detects and reports potentially fraudulent banking activities
that may stem from criminals and terrorists
Challenges
Need to analyze millions of daily transactions to identify current and potential
fraud
Move from a labor-intensive decentralized system to a more automated
process
Results
Reveals 40% more suspicious transactions by automatically identifying the
most likely fraudulent activities. Increases reporting capabilities by 200% and
analysts productivity by 80%
Discovers the latest money-laundering techniques by capturing data from 700
branches and 2,300 ATMs in six countries.
Aggregates multiple transaction activities with centralized reporting for more
precision in detecting financial relationships.
20. 20
"The IBM Data Science
Elite team was able to
help direct our operating
model, and skills,
towards a deeper and
more integrated
structure."
Guy Taylor
Head of Data & Data-Driven
Intelligence
Solution
Deployed predictive data-modeling software that helped it more easily and
quickly detect transactions that were part of potential money-laundering
operations
Solution prevents, detects and reports potentially fraudulent banking activities
that may stem from criminals and terrorists
Challenges
Fraudulent activity is very rare relative to all online banking activity, making it
difficult to predict, posing a reputational risk to the bank
Current fraudulent alert system has a very high false positive rate, lowering
customer satisfaction
Results
Reduce number of alerts that fraud responders must review and reduce missed
fraudulent activity
Assist fraud responders in identifying which suspicious activities are most likely
to be fraudulent.
21. 21
FIRST MODELING APPROACHCHALLENGES
• Fraudulent activity is very
rare relative to all online
banking activity
(0.004% of sessions)
• ~500M actions/ month
• Predictors need to be
accepted by fraud team
Nedbank:
Predict
Fraudulent
Online
Banking
Activity
SECOND MODELING APPROACH
21
OBJECTIVE
• Use supervised machine learning to
predict fraudulent activity within
Nedbank's mobile banking system
OVERVIEW
• Currently uses a decision-rule based
system to flag suspicious transactions for
review by fraud responders
• High false positive rate, low false negative
rate
• Missed fraudulent activity is costly
• Large volume of alerts places a burden on
responders
94%
48%
CURRENT SYSTEM
WITH AUGMENTATION
False Positives
4%
7%
CURRENT SYSTEM
WITH AUGMENTATION
False Negatives
95%
85%
CURRENT SYSTEM
ML MODEL
False Positives
17%
6%
CURRENT SYSTEM
ML MODEL
False Negatives
• Augment existing system by
predicting which alerts on
individual activity are correct
• Predict which user sessions are
fraudulent within first 10 seconds
22. 22
“Before this solution, the
minimum time it took to
settle a claim was three
days. Now, the low-risk
claims that pass down
the ‘immediate’ channel
can be settled within
an hour.”
Anesh Govender
Head of Finance, Reporting and
Salvage at Santam
Solution
Santam chose IBM for the range of functionality, flexibility, and its ability to
integrate with an existing system
The company’s core claims management system resided on a mainframe
platform that still met the company’s needs
The solution integrated different kinds of rules from across the infrastructure,
including process rules from company’s business process management
software system, decision and agility rules from SPSS software itself, and
override
Challenges
Fraud losses accounted for an annual 6 to 10 percent of premium costs for
Santam customers
Needed a solution that more effectively assessed risk and separated potentially
fraudulent claims from lower risk ones would prevent fraud, reduce other costs
and increase efficiency
Results
Identified a major fraud ring in less than 30 days after implementation.
Saved more than USD2.5 million in payouts to fraudulent customers, and nearly
USD5 million in total repudiations.
Reduced claims processing time on low-risk claims by nearly 90 percent.
23. 23
“The [Watson] Studio
gives us the ability to
process millions and
millions of records and
to be able to act real
time.”
Julio Sánchez
Global Analytics Lead -
Accenture Center for IBM
Technologies, Accenture
Solution
By analyzing internal company and external data to determine the
risk factors and level associated with each service user and alerts
audit managers of risky behavior
Challenges
Detects the likelihood of fraudulent behavior – such as an individual
posing as a legitimate customer who receives a service but won’t
pay for it
The ability to identify and trace new anomalous behaviors through
continuous monitoring is vital for companies to take preemptive
actions against future costly occurrences of fraud
Results
Process millions of records and be able to act real time
24. 24
Reduces payments on
fraudulent claims and
improves its ability to
collect payments from
other insurance
companies
Solution
IPCC implemented solution to rapidly identify and investigate
suspicious claims and to expedite handling of unsuspicious claims in
order to improve customer satisfaction.
Challenges
IPCC needed ways to automate the workflows and data gathering
related to fraudulent and subrogated automobile claims.
Results
Accelerated payments collection
Reduced costs of claims payments
Yielded annual return on investment (ROI) of 403% for direct and
indirect benefits and a payback within 3 months
26. Where do we go from here?
An IBM-led AI Journey Workshop provides the strategy and expertise to transform your business into a
cognitive enterprise and unlocks the full potential of your data with AI.
Briefing
& Vision
AI Journey
Workshop
Design
& Validate
Implement
& Deliver
Conclude
& Expand
Identify your unique
business challenges
and needs.
Explore how AI is
transforming
every industry.
Partake in an IBM-led
half or full day
workshop to explore
your use case and
scope out potential
solutions.
Work with IBM subject
matter experts to fully
define the scope and
success criteria for an
AI solution.
Delivery and
deployment of the
agreed upon AI
solution, tailored
specifically to your
business needs.
Explore how to further
accelerate your
organization’s AI
Journey with IBM.
Using AI/ML for fraud detection is not new. However, typical organization contains multiple fraud departments, each with its own internal point-solution which monitors fraud for that specific channel, product, or fraud type. Structured and unstructured data collected internally and externally but very few of these point-solutions share data. Each uses varying analytical techniques across channels and transaction systems, which results in not having a complete view of risk exposures across the institution. Cannot see patterns or behaviors that would spark a concern that fraudulent activity is crossing multi-business lines because the observation space is too narrow.
Combatting fraud and performing investigative action demands an end-to-end data science platform. It empowers an organization to scale analysis with ready access to public clouds, private clouds and on-premises. The platform also speeds modeling, training and deployment time and simplifies collaboration with data scientists, risk analysts, investigators, and other subject matter experts while adhering to strong governance and security posture. Further, in order to respond to new types of fraud, waste and abuse while minimizing false negatives and accelerating response, the platform needs to continuously accommodate real-time data, monitor and detect fraudulent activities and adapt as the patterns change and spot anomalies.
Rare occurrences create an imbalance in the classification of fraud detection models and makes detection challenging.
Shift to increased digital and mobile customer platforms led to transactions being executed more quickly, leaving banks and processors with less time to identify, counteract, and recover the underlying funds. As quickly as new technology is used to identify fraudsters, they themselves are identifying new ways of defrauding the bank. For instance, identity theft is mutating from card skimming to account takeovers (ATO). Synthetic identify, a scenario where fraudsters combine fragments of stolen or fake information to create a new identity and apply for financial products.
Upskill team to do more data science:
Single platform for all model development, regardless of expertise(open source coding frameworks for data scientists + visual programming tools for LOB experts and business analysts)
Align business and technical teams to work rapidly with routine against new threats
Bring together models developed from different departments and provide a 360 view of patterns and behaviors
Increase efficiency of “known” threats while continuously improving models for new threats
Faster discovery and deployment:
Support the full end to end AI lifecycle by seamlessly integrating with data management to understand your data and know where it is, deployment to get to production faster, and to protect against bias and promote trust via traceability
Fraud detection is complex and everchanging.
- To get ahead, you need deep learning – accelerated GPU (train faster), integration w/ most popular framework for deep learning (getting latest models in deep learning to stay ahead of the fraudsters), visual tools (visually build complex neural networks to train more advanced modeling) – for the data scientists
Organizations need to identify anomalies accurately and efficiently at the level of accounts, merchants, cardholders and locations.
False positives require manual investigations through providing content analytics across primary internal and external data sources
Fraud detection – meaning detecting fraudulent behavior after it occurs – forcing companies to set aside money and resources for the inevitable losses they will incur, costing financial institutions millions of dollars and destroying the customer experience. Financial institutions need to get in front of the problem and focus on fraud prevention.
Advanced analytics techniques can dramatically improve the effectiveness and efficiency of fraud management. The integration of high-quality data sources (such as digital communications, geospatial data, and satellite imagery), the use of more sophisticated modeling techniques (such as machine learning, deep learning, and natural-language processing), and the introduction of automation technologies (such as natural-language generation and cognitive-computing algorithms) are transforming the way companies approach risk management.
FASTER SCREENING UPDATES Higher detection of connected frauds thru faster updates to lists/models from monitoring analytics
ENHANCED SCREENING MODELS: Higher fraud detection thru enhanced fraud screening models
ENHANCED ACCURACY of FRAUD PROFILING Higher fraud detection thru enhanced scoring in fraud screening
ENHANCED IDENTITY VERIFICATION Higher fraud detection thru automated visual identity verification processes
CENTRALIZATION OF FRAUD PROCESSES Standardize, consolidate & automate fraud modeling across enterprise
ENHANCED FRAUD ANALYTICS TOOLS Improve productivity of fraud analysts with model building & testing acceleration tools
LOWER COST FRAUD INFRASTRUCTURE: Reduce cost of fraud analytics systems costs through use Big Data technologies
REDUCED FRAUD FALSE POSITIVE RATES: Reduce number of fraud investigations through enhanced false positive rates
IMPROVED INVESTIGATIONS PROCESS: Improve productivity of fraud investigators thru providing content analytics across primary internal and external data sources
FACILITATE INVESTIGATION CASE MANAGEMENT Improve multi-person fraud investigations through use of case management and collaboration tools
AUTOMATED FRAUD REPORTING: Reduce legal costs of fraud cases thru enhanced fraud discovery documentation
The process
Use predictive analytics to help predict the likelihood of fraud
Use data mining for clustering, classification, and segmenting data to find patterns and associations related to fraud
Use machine learning to detect anomalies in transactions and predict whether transactions are fraudulent
Use text/web mining to analyze unstructured data for sentiment analysis, or variable extraction to flag fraudulent activity
Connects insights on why fraud happens from deploy into production to predict to prevent it happening
Data science make fraud detection faster. Enabling your non-data scientists (analyst, SMEs) to train advanced ML models without DS degree – through visual modeling.
Fraud detection is complex and everchanging. To get ahead of fraudsters, you need deep learning – accelerated GPU (train faster), integration w/ most popular framework for deep learning (getting latest models in deep learning to stay ahead of the fraudsters), visual tools (visually build complex neural networks to train more advanced modeling) – for the data scientists
In the case of unstructured data (text and image), IBM is the only vendor that can provide easy tooling (train models in a few clicks, image segmentation, sentiment analysis) – pre-trained APIs, easy-to-use visual tools
Link to Reference Profile: http://w3-01.ibm.com/sales/ssi/cgi-bin/ssialias?infotype=CR&subtype=NA&htmlfid=0GLOS-87TQH2&appname=crmd#attachments
AML Compliance
Process >1.3 Million transactions / day
Predictive modeling allows the bank to narrow down the number of transactions requiring detailed analysis by 95 percent, saving resources and speeding report production.
Reduced the number of customers analyzed in each segment from 4,000 to 130, allowing for more targeted and cost-effective analysis
Solution synopsis
A bank in Colombia wants to adhere to stricter governmental regulations regarding the reporting of potentially fraudulent transactions by deploying IBM SPSS Modeler to centralize and automate its analysis of 1.3 million transactions per day; the new system can identify potential fraud more easily and more quickly, it can focus more precisely on between 5,000 and 6,000 transactions
Special handling instructions
The client has agreed to be a reference for sales situations. The status of any installation or implementation can change, so you should always contact the Primary Contact or Additional Contact named in the reference prior to discussing it with your client. Any public use, such as in marketing materials, on WWW sites, in press articles, etc., requires specific approval from the client. It is the responsibility of the person or any organization planning to use this reference to make sure that this is done. The IBM representative will, as appropriate, contact the client for review. You should not contact the client directly.
Business need
Bancolombia, a private bank based in Medellin, Colombia, serves 6 million customers in six countries. It needs to adhere to stricter governmental reporting requirements instituted in 2008, and to analyze millions of daily transactions to identify current and potential fraud. With its decentralized system, staff has to routinely analyze 120,000 customers and transactions per week. The bank wants to evolve from that labor-intensive decentralized system based on strict rules and parameters to a more automated one that would better detect unusual patterns or behavior.
Solution implementation
Bancolombia deployed IBM SPSS Modeler to improve its ability to identify potential money-laundering and other fraudulent activities. It increased the speed and precision of its compliance reporting, integrated and centralized data from its multiple branches and its lines of business, and substantially lowered the cost of analyzing individual transactions. It can now identify transactional activities that may have been distributed among multiple entities in order to circumvent statutory limits and currency regulations.
Benefits of the solution
By using IBM SPSS Modeler, Bancolombia was able to reduce the number of transactions it analyzed from 120,000 per week to between 5,000 and 6,000. By reducing the number of transactions it had to analyze, it was able to generate productivity savings of up to 80 percent. The increased efficiency it gained from the use of IBM SPSS Modeler also enabled it to increase the number of “suspicious operation” reports it files with the government from 400 to 1,200. At the same time, it has been able to submit reports of higher quality, which gives the government more information to pursue potential fraud. Previously, only 57 percent of the bank’s reports received the highest ratings in terms of quality and thoroughness. With the new system, 97 percent of the reports receive the highest rating.What Makes it Smarter: - Intelligent: Reveals 40 percent more suspicious transactions by automatically identifying the most like fraudulent activities. Increases reporting capabilities by 200 percent and analysis productivity by 80 percent. - Instrumented: Discovers the latest money-laundering techniques by capturing account data from 700 branches and 2,300 ATMs in six countries. - Interconnnected: Aggregates multiple transaction activities with centralized reporting for more precision in detecting financial relationships.Additional Smarter Planet information:- Intelligent: Reveals 40 percent more suspicious transactions by automatically mining 1.3 million transactions per day and identifying the most transactions most likely to be fraudulent. Previously, bank employees manually analyzed 120,000 customers and transactions per week. Because the new system can identify potential fraud more easily and more quickly, it can focus more precisely on a smaller number of transactions—between 5,000 and 6,000. Additional efficiency gains can be found in how the automated solution generated productivity savings of nearly 80 percent by reducing the number of staff needed to review massive transaction volume, while increasing reporting by 200 percent. - Instrumented: Discovers the latest money-laundering techniques and increases accuracy by capturing data from both commercial and personal accounts, and from 700 branches and 2,300 ATMs in six countries. The bank uses two specific identifiers crucial to understanding deviations: expected transactional patterns for different commercial segments, and normal transaction patterns for individual customers within each segment. By defining expected and typical patterns, the bank can then mine the data to identify either unusual transactions or sudden changes in behavior. - Interconnected: Aggregates activities that may be distributed among multiple entities in order to circumvent statutory limits and currency regulations by centralizing reporting from the bank’s 700 branches. This allows the bank to more precisely detect relationships between those who deposit money and those who receive money.Solutions/Offerings
Special handling instructions
The client has agreed to be a reference for sales situations. The status of any installation or implementation can change, so you should always contact the Primary Contact or Additional Contact named in the reference prior to discussing it with your client.
Link to Reference Profile: http://w3-01.ibm.com/sales/ssi/cgi-bin/ssialias?infotype=RF&subtype=CS&htmlfid=SANS-985HX2&appname=crmd
Public Case Study: http://www-03.ibm.com/software/businesscasestudies?synkey=P366760E07052S87
Client Name: Santam Insurance
About the client - Santam is South Africa’s largest short-term insurance company with assets of over R17 billion (US$ 1.88 billion). It provides personal, commercial, agricultural, and specialist insurance policies throughout South Africa and holds additional businesses in Zimbabwe, Malawi, Uganda, Tanzania and Zambia
Business Need:Santam faced the challenge of operating in an environment where fraud was estimated to account for between 6 and 10 percent of all premium revenue because of the challenges of managing complex claims while maintaining a high level of customer service. To solve this problem, Santam sought to find a more personalized method for managing each claim and prioritizing the effort needed to successfully investigate and mediate each claim. Solution Summary:The Head of Finance, Reporting and Salvage at Santam, sought to automate and manage these claims through a predictive analytics solution. Santam’s vendor evaluation process led to a decision to select either SAS Institute or Olrac SPSolutions (an IBM Partner), which was offering an IBM SPSS-based solution. Santam sought to create an advanced predictive analytics deployment and were sure that Olrac SPSolutions had the skills and prior experience necessary to build the necessary functionality from an IBM SPSS base solution. Results:Saved R17.9 million (US$ 1.98 million) in the first four months of use Benefits:The detection of an insurance fraud syndicate that had previously gone undetected; The use of predictive analytics to categorize claims also reduced the time and costassociated with settling cases; Low risk cases no longer have to go through the exhaustive due diligence that previously took at least three days to perform. Now, approximately 50 percent of these claims are accelerated through improved categorization. Fifteen percent of claims, or about 54,000 claims, can be processed in less than an hour, representing a 95 percent savings in time
https://www.ibm.com/case-studies/Accenture
Fraud detection in the telecommunications space is a major focus area for Accenture’s business.
Infinity Property & Casualty Corporation (IPCC) is a provider of personal automobile insurance with an emphasis on nonstandard auto insurance.
Nonstandard auto insurance provides coverage to drivers who, because of their driving record, age, or vehicle type, represent higher than normal risks and pay higher rates for coverage. The company’s products provide insurance coverage for liability to others for bodily injury and property damage, and for physical damage to an insured’s vehicle from collision and various other damages. IPCC distributes its products primarily through the Web and a network of independent agencies.
Connects insights on why fraud happens from deploy into production to predict to prevent it happening
Data science make fraud detection faster. Enabling your non-data scientists (analyst, SMEs) to train advanced ML models without DS degree – through visual modeling.
Fraud detection is complex and everchanging. To get ahead of fraud, you need deep learning – accelerated GPU (train faster), integration w/ most popular framework for deep learning (getting latest models in deep learning to stay ahead of the fraudsters), visual tools (visually build complex neural networks to train more advanced modeling) – for the data scientists
In the case of unstructured data (text and image), IBM is the only vendor that can provide easy tooling (train models in a few clicks, image segmentation, sentiment analysis) – pre-trained APIs, easy-to-use visual tools
An IBM-led AI Journey Workshop provides the strategy and expertise to transform your business into a cognitive enterprise and unlocks the full potential of your data with AI. AI Journey Workshops are complimentary, from IBM to you. • Develop an actionable use case and roadmap to the future. • Design a high-level solution in support of the given use case. • Identify gaps and plan to address through detailed design.
Once the backdrop of the auto insurance claims triaging story has been established, a best practice for this demo is to show the end result of what’s being built so your audience knows what they’re heading toward.
In this case, what we’re using Watson Studio for is to explore the available data from the insurance company’s system of record to pull out data of interest for the claims adjusters, and to build, train, and deploy a claims fraud probability model. We’re also taking the location data and using it to get weather data for the date/time of the loss event, and plotting the locations of interest onto a map.
Watson Studio features self-service tools designed for many different kinds of knowledge workers, ranging from business analysts who look for GUI tools, with MS Excel-like functionality, and data scientists who need tools to make the development and training of neural nets easier. There are two important points here:
- teams can mix and match tooling (ie. Use refinery to prepare a data set, and train a model with this prepared data set in a notebook or canvas tool
- all of these tools share a common environment, with governance tools, security, and management interfaces. No longer do people need to throw work over the fence by a team with specialized skills; now, these skilled users are in the same platform, where it’s easy for people to share results and work together.