In today's digital world, credit card fraud is a growing concern. This project explores machine learning techniques for credit card fraud detection. We delve into building models that can identify suspicious transactions in real-time, protecting both consumers and financial institutions. for more detection and machine learning algorithm explore data science and analysis course: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
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 document analyzes a dataset of over 280,000 credit card transactions to detect fraudulent transactions using machine learning techniques. It first discusses challenges in credit card fraud detection like imbalanced data and lack of standard evaluation metrics. It then evaluates techniques like support vector machines, random forests, and local outlier factors. Analysis of the dataset found the data is highly skewed with few fraud cases. While models could achieve high accuracy by predicting all transactions as valid, other metrics are needed. The document concludes by implementing a local outlier factor model to detect patterns in fraudulent transactions, though accuracy in detecting fraud was low.
The document discusses building a machine learning model to predict customer churn for a telecommunications company using a dataset containing customer characteristics. It describes preprocessing the data, exploring the features, training various classification models including logistic regression, support vector machines, random forests and decision trees, and evaluating model performance. Logistic regression achieved the best results with 79% accuracy at predicting whether customers will churn. Future work could include reducing more features and testing additional models to improve accuracy for predicting telecom customer churn.
This document discusses predicting customer churn for a telecommunications company. It begins with an introduction to the problem and dataset, which contains information on 7,043 customers. It then preprocesses the data, which has 19 variables on demographic, account, and service characteristics. Various machine learning algorithms are trained and evaluated on the data, with logistic regression achieving the best accuracy of 79%. The document concludes with opportunities for future improvement and acknowledgments.
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
Traditional fraud prevention tools like business rules, data mining, and neural networks have failed to reduce fraud losses over the last 20 years because they rely on historical data and predefined rules that cannot adapt to continuously evolving fraud schemes. Next-generation real-time fraud prevention requires an approach that does not rely exclusively on predefined rules, can analyze individual behaviors, provides multiple layers of protection across different channels, and can adaptively learn over time to maximize profitability while minimizing fraud losses. Smart agent technology provides this by creating unique profiles for each entity, learning from their activities in real-time across all relevant data and channels, and sharing this intelligence to more effectively prevent new fraud schemes from occurring.
An Identification and Detection of Fraudulence in Credit Card Fraud Transacti...IRJET Journal
This document summarizes a research paper that proposes a system for detecting fraudulent credit card transactions using data mining techniques. The system uses the Apriori algorithm to perform frequent item set mining on a credit card transaction dataset. It then uses the Support Vector Machine (SVM) classification method to match new transactions to either a legal transaction pattern database or a fraudulent transaction pattern database that was formed based on users' previous transactions. The results showed this proposed method achieved better fraud detection with a lower false alarm rate than existing methods like Hidden Markov Models.
Explore our students' cutting-edge project on predicting bank customer churn using advanced analytics techniques. This project employs machine learning algorithms to analyze customer data and forecast the likelihood of churn, offering valuable insights for financial institutions. Gain insights into customer retention strategies, predictive modeling, and the potential impact on banking operations. To learn more, do check out https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
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 document analyzes a dataset of over 280,000 credit card transactions to detect fraudulent transactions using machine learning techniques. It first discusses challenges in credit card fraud detection like imbalanced data and lack of standard evaluation metrics. It then evaluates techniques like support vector machines, random forests, and local outlier factors. Analysis of the dataset found the data is highly skewed with few fraud cases. While models could achieve high accuracy by predicting all transactions as valid, other metrics are needed. The document concludes by implementing a local outlier factor model to detect patterns in fraudulent transactions, though accuracy in detecting fraud was low.
The document discusses building a machine learning model to predict customer churn for a telecommunications company using a dataset containing customer characteristics. It describes preprocessing the data, exploring the features, training various classification models including logistic regression, support vector machines, random forests and decision trees, and evaluating model performance. Logistic regression achieved the best results with 79% accuracy at predicting whether customers will churn. Future work could include reducing more features and testing additional models to improve accuracy for predicting telecom customer churn.
This document discusses predicting customer churn for a telecommunications company. It begins with an introduction to the problem and dataset, which contains information on 7,043 customers. It then preprocesses the data, which has 19 variables on demographic, account, and service characteristics. Various machine learning algorithms are trained and evaluated on the data, with logistic regression achieving the best accuracy of 79%. The document concludes with opportunities for future improvement and acknowledgments.
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
Traditional fraud prevention tools like business rules, data mining, and neural networks have failed to reduce fraud losses over the last 20 years because they rely on historical data and predefined rules that cannot adapt to continuously evolving fraud schemes. Next-generation real-time fraud prevention requires an approach that does not rely exclusively on predefined rules, can analyze individual behaviors, provides multiple layers of protection across different channels, and can adaptively learn over time to maximize profitability while minimizing fraud losses. Smart agent technology provides this by creating unique profiles for each entity, learning from their activities in real-time across all relevant data and channels, and sharing this intelligence to more effectively prevent new fraud schemes from occurring.
An Identification and Detection of Fraudulence in Credit Card Fraud Transacti...IRJET Journal
This document summarizes a research paper that proposes a system for detecting fraudulent credit card transactions using data mining techniques. The system uses the Apriori algorithm to perform frequent item set mining on a credit card transaction dataset. It then uses the Support Vector Machine (SVM) classification method to match new transactions to either a legal transaction pattern database or a fraudulent transaction pattern database that was formed based on users' previous transactions. The results showed this proposed method achieved better fraud detection with a lower false alarm rate than existing methods like Hidden Markov Models.
Explore our students' cutting-edge project on predicting bank customer churn using advanced analytics techniques. This project employs machine learning algorithms to analyze customer data and forecast the likelihood of churn, offering valuable insights for financial institutions. Gain insights into customer retention strategies, predictive modeling, and the potential impact on banking operations. To learn more, do check out https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
A Review of deep learning techniques in detection of anomaly incredit card tr...IRJET Journal
This document summarizes a review of deep learning techniques for detecting anomalies in credit card transactions. It discusses how credit card fraud causes major financial losses and how machine learning can help identify fraudulent transactions. The document outlines the objectives of comparing support vector machines and random forests for credit card fraud detection and discusses challenges like class imbalance in the data. It presents the system architecture for credit card fraud detection and analyzes results on a dataset of European credit card transactions, finding random forests outperform decision trees. Future work to improve accuracy is also discussed.
Machine Learning-Based Approaches for Fraud Detection in Credit Card Transact...IRJET Journal
This document presents a comparative study of machine learning approaches for credit card fraud detection. It explores isolation forest, local outlier factor (LOF), and one-class support vector machine (SVM) algorithms for fraud detection and compares their performance. The document first collects a dataset of credit card transactions containing both legitimate and fraudulent examples. It then implements and evaluates the machine learning models, assessing their ability to accurately identify fraud while minimizing false positives. Statistical tests and visualization techniques like ROC curves are used to analyze the models' performance. The best-performing aspects of each model are identified to inform optimal fraud detection.
- The document describes a project to predict customer churn for a telecom company using classification algorithms. It analyzes a dataset of 3333 customers to identify variables that contribute to churn and builds models using KNN and C4.5.
- The C4.5 model achieved higher accuracy (94.9%) than KNN (87.1%) on the test data. Key variables for predicting churn were found to be day minutes, customer service calls, and international plan.
- The model can help the telecom company prevent churn by focusing retention efforts on at-risk customers identified through these important variables.
This document provides an overview of machine learning and logistic regression. It discusses key concepts in machine learning like representation, evaluation, and optimization. It also discusses different machine learning algorithms like decision trees, neural networks, and support vector machines. The document then focuses on logistic regression, explaining concepts like maximum likelihood estimation, concordance, and confusion matrices which are used to evaluate logistic regression models. It provides an example of using logistic regression for a banking customer classification problem to predict defaults.
network layer service models forwarding versus routing how a router works rou...Ashish Gupta
Here are the key types of unsupervised learning algorithms:
- Clustering: Groups unlabeled data points that are similar to each other. Examples include K-means clustering, hierarchical clustering.
- Association Rule Learning: Finds relationships between variables in large datasets. Apriori algorithm is commonly used.
- Dimensionality Reduction: Reduces the number of random variables in the data. Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) are popular techniques.
- Anomaly Detection: Finds unusual data points that do not fit a expected distribution or pattern. Isolation Forest algorithm is often used.
- Representation Learning: Learns representations of the data
CREDIT CARD FRAUD DETECTION USING MACHINE LEARNINGIRJET Journal
The document discusses using machine learning algorithms, specifically the Random Forest algorithm, to detect credit card fraud. It begins with an abstract that outlines how machine learning can be used to analyze large amounts of transaction data and detect fraudulent patterns. The document then provides background on the challenges of credit card fraud and how machine learning is being increasingly used to identify fraudulent transactions. It proposes using the Random Forest algorithm for credit card fraud detection as it can effectively handle large datasets, non-linear relationships between features, and provide important feature analysis. The document discusses preprocessing data, feature engineering, handling imbalanced data, training the Random Forest model, and evaluating performance based on metrics like accuracy, precision, recall and F1 score. It finds that Random Forest achieved
Sereno is a fraud detection solution that uses image analysis and multi-source correlation modeling to identify check fraud. It integrates with existing image processing systems and analyzes check images using multiple recognition engines to flag potential fraud. Sereno reduces false positives and focuses analysts on a small number of suspect transactions. It builds databases of check stock and signatures over time to improve accuracy. Sereno provides cost savings through reduced manual review and losses from fraud while allowing banks to expand their fraud detection capabilities.
This document discusses conducting a "Reconnaissance Check" of a company's telecommunications infrastructure to identify opportunities to improve operations and lower costs. It states that thorough reconnaissance can uncover multiple factors representing 35% or more in potential savings. The document advocates using techniques from military intelligence, surveillance, and reconnaissance (ISR) to understand network traffic flows, bottlenecks, services, and market prices in order to optimize the network configuration and carrier contracts. Quantitative metrics should be established to measure the network's performance and identify outliers that may indicate inefficiencies.
This document summarizes research on detecting financial fraud in healthcare using machine and deep learning. It discusses how algorithms like random forest, decision trees, logistic regression, and neural networks can be used to identify fraudulent credit card transactions. The document outlines several research papers that experimented with different models on real transaction data. It then describes how the experiments were set up, evaluating models like logistic regression, naive Bayes, KNN and sequential models on a dataset from credit card transactions in Taiwan. The sequential model achieved the best performance based on evaluation metrics like accuracy, sensitivity and precision. There is potential to improve results further by optimizing hyperparameters and using transfer learning.
IRJET- Fraud Detection Algorithms for a Credit CardIRJET Journal
This document discusses algorithms for detecting credit card fraud. It compares the performance of two algorithms: random forest and K-nearest neighbors (KNN). Random forest uses decision trees to classify transactions as normal or fraudulent based on attributes of past transactions. KNN compares new transactions to historical ones based on attributes. The document tests these algorithms on a real-world credit card transaction dataset. It finds that random forest obtains good results on smaller datasets but has issues with imbalanced data. The authors' future work will focus on addressing these issues and improving the random forest algorithm.
MACHINE LEARNING ALGORITHMS FOR CREDIT CARD FRAUD DETECTIONmlaij
Fraud is a critical issue in our society today. Losses due to payment fraud are on the increase as ecommerce keeps evolving. Organizations, governments, and individuals have experienced huge losses due
to payment. Merchant Savvy projects that global losses due to payment fraud will increase to about $40.62
billion in 2027 . Among all payment fraud, credit card fraud results in a higher loss. Therefore, we intend
to leverage the potential of machine learning to deal with the problem of fraud in credit cards which can
be generalized to other fraud types. This paper compares the performance of logistic regression, decision
trees, random forest classifier, isolation forest, local outlier factor, and one-class support vector machines
(SVM) based on their AUC and F1-score. We applied a smote technique to handle the imbalanced nature
of the data and compared the performance of the supervised models on the oversampled data to the raw
data. From the results, the Random Forest classifier outperformed the other models with a higher AUC
score and better f1-score on both the actual and oversampled data. Oversampling the data didn't change
the result of the decision trees. One-class SVM performs better than isolation forest in terms of AUC score
but has a very low f1-score compared to isolation forest. The local outlier factor had the poorest
performance.
Dive deep into the world of insurance churn prediction with this captivating data analysis project presented by Boston Institute of Analytics. Our talented students embark on a journey to unravel the mysteries behind customer churn in the insurance industry, leveraging advanced data analysis techniques to forecast and anticipate customer behavior. From analyzing historical data and customer demographics to identifying predictive indicators and developing churn prediction models, this project offers a comprehensive exploration of the factors influencing insurance churn dynamics. Gain valuable insights and actionable recommendations derived from rigorous data analysis, presented in an engaging and informative format. Don't miss this opportunity to delve into the fascinating realm of data analysis and unlock new perspectives on insurance churn prediction. Explore the project now and embark on a journey of discovery with Boston Institute of Analytics. To learn more about our data science and artificial intelligence programs, visit https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/.
network layer service models forwarding versus routing how a router works rou...Ashish Gupta
Here are the key types of unsupervised learning algorithms:
- Clustering: Groups unlabeled data points that are similar to each other. Examples include K-means clustering, hierarchical clustering.
- Association Rule Learning: Finds relationships between variables in large datasets. It is intended to identify strong rules discovered in databases using some measures of interestingness.
- Dimensionality Reduction: Reduces the number of random variables under consideration by obtaining a set of principal variables. Examples include principal component analysis, linear discriminant analysis.
- Anomaly Detection: Detects outliers and anomalies in data. It forms the basis for fraud detection.
- Neural Networks: Finds complex patterns in data through a process of learning rather than programming.
The document discusses credit card fraud detection. It defines credit card fraud as unauthorized purchases made using someone's credit card or account. Credit card fraud detection models past credit card transactions to identify fraudulent versus legitimate transactions. The model's performance is evaluated based on metrics like true positives, false positives, accuracy, sensitivity, specificity, and precision. The dataset used contains over 284,000 credit card transactions, with variables like amount and time, and a class variable indicating legitimate or fraudulent transactions. An XGBoost model is used for fraud prediction in the user interface. XGBoost is an optimized gradient boosting algorithm that converts weak learners into strong learners through sequential iterations to improve predictions.
The document describes using a random forest algorithm to detect credit card fraud. It begins with an abstract that outlines analyzing a credit card dataset, applying random forest, and identifying fraud transactions with 98% accuracy. Existing methods are discussed that achieve 60-70% accuracy. The proposed system uses random forest classification to analyze the dataset, which can process large amounts of data quickly and achieve 98% accuracy. Literature on the topic is surveyed. Random forest and the system architecture are described in more detail, including modules for data collection, preprocessing, feature extraction, model evaluation and visualization of results. The random forest model achieves 98.6% accuracy, outperforming other methods. Conclusions discuss potential improvements like using more data and preprocessing techniques.
This document presents a seminar on a credit card fraud detection model based on the Apriori algorithm. The model uses frequent itemset mining to find legal and fraudulent transaction patterns for each customer, converting an imbalanced credit card transaction dataset into a balanced one. The model is trained using Apriori to generate legal and fraud transaction patterns for each customer. New transactions are then matched to these patterns to detect fraud. The proposed model works independently of attribute values and can handle class imbalance issues common in fraud detection.
ML & Graph algorithms to prevent financial crime in digital paymentsData Science Milan
This document discusses using machine learning and graph algorithms to prevent financial crime in digital payments. It presents a three level approach: Level 0 uses rule-based SQL queries to detect anomalies, Level 1 applies supervised machine learning to classify transactions, and Level 2 uses a graph database and rules to model network anomalies. Level 3 combines machine learning, graph algorithms, and personalized page rank to spread anomaly scores throughout a transaction network to identify suspicious groups. The strategies are being piloted through the Infinitech Project to develop technologies for applications in financial crime prevention, cybersecurity, and personalized products using AI, big data, IoT, and blockchain.
Churn in the Telecommunications Industryskewdlogix
Strategic Business Analysis Capstone Project Telecommunications Churn Management
Churn is a significant problem that costs telecommunications companies billions of dollars through lost revenue. Now that the market is more mature, the only way for a company to grow is to take their competitors customers. This issue
combined with the greater choice that consumers have gained means that any adverse touch point with a consumer can result in a lost customer.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
This presentation explores how K-means clustering can be used to analyze solar production data and identify patterns that can help optimize energy generation. visit https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/ for more
Weitere ähnliche Inhalte
Ähnlich wie Credit Card Fraud Detection: Safeguarding Transactions in the Digital Age
A Review of deep learning techniques in detection of anomaly incredit card tr...IRJET Journal
This document summarizes a review of deep learning techniques for detecting anomalies in credit card transactions. It discusses how credit card fraud causes major financial losses and how machine learning can help identify fraudulent transactions. The document outlines the objectives of comparing support vector machines and random forests for credit card fraud detection and discusses challenges like class imbalance in the data. It presents the system architecture for credit card fraud detection and analyzes results on a dataset of European credit card transactions, finding random forests outperform decision trees. Future work to improve accuracy is also discussed.
Machine Learning-Based Approaches for Fraud Detection in Credit Card Transact...IRJET Journal
This document presents a comparative study of machine learning approaches for credit card fraud detection. It explores isolation forest, local outlier factor (LOF), and one-class support vector machine (SVM) algorithms for fraud detection and compares their performance. The document first collects a dataset of credit card transactions containing both legitimate and fraudulent examples. It then implements and evaluates the machine learning models, assessing their ability to accurately identify fraud while minimizing false positives. Statistical tests and visualization techniques like ROC curves are used to analyze the models' performance. The best-performing aspects of each model are identified to inform optimal fraud detection.
- The document describes a project to predict customer churn for a telecom company using classification algorithms. It analyzes a dataset of 3333 customers to identify variables that contribute to churn and builds models using KNN and C4.5.
- The C4.5 model achieved higher accuracy (94.9%) than KNN (87.1%) on the test data. Key variables for predicting churn were found to be day minutes, customer service calls, and international plan.
- The model can help the telecom company prevent churn by focusing retention efforts on at-risk customers identified through these important variables.
This document provides an overview of machine learning and logistic regression. It discusses key concepts in machine learning like representation, evaluation, and optimization. It also discusses different machine learning algorithms like decision trees, neural networks, and support vector machines. The document then focuses on logistic regression, explaining concepts like maximum likelihood estimation, concordance, and confusion matrices which are used to evaluate logistic regression models. It provides an example of using logistic regression for a banking customer classification problem to predict defaults.
network layer service models forwarding versus routing how a router works rou...Ashish Gupta
Here are the key types of unsupervised learning algorithms:
- Clustering: Groups unlabeled data points that are similar to each other. Examples include K-means clustering, hierarchical clustering.
- Association Rule Learning: Finds relationships between variables in large datasets. Apriori algorithm is commonly used.
- Dimensionality Reduction: Reduces the number of random variables in the data. Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) are popular techniques.
- Anomaly Detection: Finds unusual data points that do not fit a expected distribution or pattern. Isolation Forest algorithm is often used.
- Representation Learning: Learns representations of the data
CREDIT CARD FRAUD DETECTION USING MACHINE LEARNINGIRJET Journal
The document discusses using machine learning algorithms, specifically the Random Forest algorithm, to detect credit card fraud. It begins with an abstract that outlines how machine learning can be used to analyze large amounts of transaction data and detect fraudulent patterns. The document then provides background on the challenges of credit card fraud and how machine learning is being increasingly used to identify fraudulent transactions. It proposes using the Random Forest algorithm for credit card fraud detection as it can effectively handle large datasets, non-linear relationships between features, and provide important feature analysis. The document discusses preprocessing data, feature engineering, handling imbalanced data, training the Random Forest model, and evaluating performance based on metrics like accuracy, precision, recall and F1 score. It finds that Random Forest achieved
Sereno is a fraud detection solution that uses image analysis and multi-source correlation modeling to identify check fraud. It integrates with existing image processing systems and analyzes check images using multiple recognition engines to flag potential fraud. Sereno reduces false positives and focuses analysts on a small number of suspect transactions. It builds databases of check stock and signatures over time to improve accuracy. Sereno provides cost savings through reduced manual review and losses from fraud while allowing banks to expand their fraud detection capabilities.
This document discusses conducting a "Reconnaissance Check" of a company's telecommunications infrastructure to identify opportunities to improve operations and lower costs. It states that thorough reconnaissance can uncover multiple factors representing 35% or more in potential savings. The document advocates using techniques from military intelligence, surveillance, and reconnaissance (ISR) to understand network traffic flows, bottlenecks, services, and market prices in order to optimize the network configuration and carrier contracts. Quantitative metrics should be established to measure the network's performance and identify outliers that may indicate inefficiencies.
This document summarizes research on detecting financial fraud in healthcare using machine and deep learning. It discusses how algorithms like random forest, decision trees, logistic regression, and neural networks can be used to identify fraudulent credit card transactions. The document outlines several research papers that experimented with different models on real transaction data. It then describes how the experiments were set up, evaluating models like logistic regression, naive Bayes, KNN and sequential models on a dataset from credit card transactions in Taiwan. The sequential model achieved the best performance based on evaluation metrics like accuracy, sensitivity and precision. There is potential to improve results further by optimizing hyperparameters and using transfer learning.
IRJET- Fraud Detection Algorithms for a Credit CardIRJET Journal
This document discusses algorithms for detecting credit card fraud. It compares the performance of two algorithms: random forest and K-nearest neighbors (KNN). Random forest uses decision trees to classify transactions as normal or fraudulent based on attributes of past transactions. KNN compares new transactions to historical ones based on attributes. The document tests these algorithms on a real-world credit card transaction dataset. It finds that random forest obtains good results on smaller datasets but has issues with imbalanced data. The authors' future work will focus on addressing these issues and improving the random forest algorithm.
MACHINE LEARNING ALGORITHMS FOR CREDIT CARD FRAUD DETECTIONmlaij
Fraud is a critical issue in our society today. Losses due to payment fraud are on the increase as ecommerce keeps evolving. Organizations, governments, and individuals have experienced huge losses due
to payment. Merchant Savvy projects that global losses due to payment fraud will increase to about $40.62
billion in 2027 . Among all payment fraud, credit card fraud results in a higher loss. Therefore, we intend
to leverage the potential of machine learning to deal with the problem of fraud in credit cards which can
be generalized to other fraud types. This paper compares the performance of logistic regression, decision
trees, random forest classifier, isolation forest, local outlier factor, and one-class support vector machines
(SVM) based on their AUC and F1-score. We applied a smote technique to handle the imbalanced nature
of the data and compared the performance of the supervised models on the oversampled data to the raw
data. From the results, the Random Forest classifier outperformed the other models with a higher AUC
score and better f1-score on both the actual and oversampled data. Oversampling the data didn't change
the result of the decision trees. One-class SVM performs better than isolation forest in terms of AUC score
but has a very low f1-score compared to isolation forest. The local outlier factor had the poorest
performance.
Dive deep into the world of insurance churn prediction with this captivating data analysis project presented by Boston Institute of Analytics. Our talented students embark on a journey to unravel the mysteries behind customer churn in the insurance industry, leveraging advanced data analysis techniques to forecast and anticipate customer behavior. From analyzing historical data and customer demographics to identifying predictive indicators and developing churn prediction models, this project offers a comprehensive exploration of the factors influencing insurance churn dynamics. Gain valuable insights and actionable recommendations derived from rigorous data analysis, presented in an engaging and informative format. Don't miss this opportunity to delve into the fascinating realm of data analysis and unlock new perspectives on insurance churn prediction. Explore the project now and embark on a journey of discovery with Boston Institute of Analytics. To learn more about our data science and artificial intelligence programs, visit https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/.
network layer service models forwarding versus routing how a router works rou...Ashish Gupta
Here are the key types of unsupervised learning algorithms:
- Clustering: Groups unlabeled data points that are similar to each other. Examples include K-means clustering, hierarchical clustering.
- Association Rule Learning: Finds relationships between variables in large datasets. It is intended to identify strong rules discovered in databases using some measures of interestingness.
- Dimensionality Reduction: Reduces the number of random variables under consideration by obtaining a set of principal variables. Examples include principal component analysis, linear discriminant analysis.
- Anomaly Detection: Detects outliers and anomalies in data. It forms the basis for fraud detection.
- Neural Networks: Finds complex patterns in data through a process of learning rather than programming.
The document discusses credit card fraud detection. It defines credit card fraud as unauthorized purchases made using someone's credit card or account. Credit card fraud detection models past credit card transactions to identify fraudulent versus legitimate transactions. The model's performance is evaluated based on metrics like true positives, false positives, accuracy, sensitivity, specificity, and precision. The dataset used contains over 284,000 credit card transactions, with variables like amount and time, and a class variable indicating legitimate or fraudulent transactions. An XGBoost model is used for fraud prediction in the user interface. XGBoost is an optimized gradient boosting algorithm that converts weak learners into strong learners through sequential iterations to improve predictions.
The document describes using a random forest algorithm to detect credit card fraud. It begins with an abstract that outlines analyzing a credit card dataset, applying random forest, and identifying fraud transactions with 98% accuracy. Existing methods are discussed that achieve 60-70% accuracy. The proposed system uses random forest classification to analyze the dataset, which can process large amounts of data quickly and achieve 98% accuracy. Literature on the topic is surveyed. Random forest and the system architecture are described in more detail, including modules for data collection, preprocessing, feature extraction, model evaluation and visualization of results. The random forest model achieves 98.6% accuracy, outperforming other methods. Conclusions discuss potential improvements like using more data and preprocessing techniques.
This document presents a seminar on a credit card fraud detection model based on the Apriori algorithm. The model uses frequent itemset mining to find legal and fraudulent transaction patterns for each customer, converting an imbalanced credit card transaction dataset into a balanced one. The model is trained using Apriori to generate legal and fraud transaction patterns for each customer. New transactions are then matched to these patterns to detect fraud. The proposed model works independently of attribute values and can handle class imbalance issues common in fraud detection.
ML & Graph algorithms to prevent financial crime in digital paymentsData Science Milan
This document discusses using machine learning and graph algorithms to prevent financial crime in digital payments. It presents a three level approach: Level 0 uses rule-based SQL queries to detect anomalies, Level 1 applies supervised machine learning to classify transactions, and Level 2 uses a graph database and rules to model network anomalies. Level 3 combines machine learning, graph algorithms, and personalized page rank to spread anomaly scores throughout a transaction network to identify suspicious groups. The strategies are being piloted through the Infinitech Project to develop technologies for applications in financial crime prevention, cybersecurity, and personalized products using AI, big data, IoT, and blockchain.
Churn in the Telecommunications Industryskewdlogix
Strategic Business Analysis Capstone Project Telecommunications Churn Management
Churn is a significant problem that costs telecommunications companies billions of dollars through lost revenue. Now that the market is more mature, the only way for a company to grow is to take their competitors customers. This issue
combined with the greater choice that consumers have gained means that any adverse touch point with a consumer can result in a lost customer.
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Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
This presentation explores how K-means clustering can be used to analyze solar production data and identify patterns that can help optimize energy generation. visit https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/ for more
This presentation dives into the world of data science and explores its application in predicting salary ranges. We'll uncover the secrets hidden within data sets, unveil the power of machine learning algorithms, and shed light on factors that influence salaries in today's job market.
Visit for more https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
This presentation explores the potential of machine learning in predicting the severity of road accidents. We will delve into the data analysis process, the chosen machine learning algorithms, and the evaluation of our model's performance. This project aims to contribute to improved emergency response times and accident prevention strategies. visit for more: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Explore how our student team leveraged data science to forecast power consumption, empowering smarter energy management and sustainability initiatives. visit for more: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Delve into the realm of sensor networks and uncover the sophisticated techniques employed for anomaly detection and event prediction. From statistical analysis to machine learning algorithms, explore how these technologies empower proactive decision-making in various domains, including industrial monitoring, environmental sensing, and healthcare systems. To learn more about detection and other techniques visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Explore the cutting-edge methods and technologies utilized in rain forecasting, from traditional meteorological models to machine learning algorithms. Discover how these predictive tools enable accurate anticipation of rainfall patterns, aiding in disaster preparedness, agriculture planning, and urban infrastructure management. To learn in detail about analysis and prediction visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Ever wondered what factors influence house prices? This project explores the world of house price prediction using data science techniques. We delve into analyzing real estate data to build models that can estimate the value of a home. This can be a valuable tool for both buyers and sellers navigating the housing market. visit https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/ for more details
This project explores sentiment analysis, a technique used to understand emotions expressed in text. We delve into the world of movie reviews, applying sentiment analysis techniques to uncover audience sentiment towards various films. This can provide valuable insights for filmmakers, studios, and moviegoers alike. For more analysis and artificial intelligence related content visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
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https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
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Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
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Credit Card Fraud Detection: Safeguarding Transactions in the Digital Age
1. Securing Financial Transactions:
Credit Card Fraud Detection
Advancing Financial Security and Prevention
Through Machine Learning Innovations
Kamakshi Sharma
Data enthusiast and lifelong learner ✨
2. Did you know credit card fraud affects millions globally
each year?
This widespread criminal activity leads to financial losses and identity theft
for consumers, while businesses face chargebacks and reputational
damage. Secure financial transactions are the bedrock of trust in today's
digital economy.
This project tackles the critical challenge of credit card fraud detection and
prevention.
Our goal is to develop effective methods using machine learning, anomaly
detection, and deep learning to identify fraudulent activities.
Objective : Enhancing financial transaction security and minimizing
fraudulent losses.
3. DATASET DESCRIPTION
This project leverages a simulated credit card transaction dataset encompassing the
period from January 1st, 2019, to December 31st, 2020. The data provides valuable
insights into both legitimate and fraudulent transactions, enabling us to develop
robust fraud detection methods.
Key dataset specifications:1296675 rows & 23 columns
The dataset includes these attributes:
Column Names Description
Transaction
Details
trans_date_trans_time, trans_num,
unix_time
Transaction date, time,
number, and Unix timestamp
Card Information cc_num Credit card number
Merchant Details
merchant, category, amt, merch_lat,
merch_long
Merchant's information and
transaction details
Customer Details
first, last, gender, street, city, state, zip,
lat, long, city_pop, job, dob
Customer's information and
transaction details
Fraud Indicator is_fraud
Indicates whether the
transaction is fraudulent (1
for fraud, 0 for legitimate)
4. OVERVIEW
In this project, I aimed enhance financial transaction security and minimize fraudulent
losses using machine learning techniques, anomaly detection technique, and deep learning technique.
Where, I performed extensive data analysis, including exploratory data analysis (EDA) to
understand the characteristics of the dataset and to do data cleaning, and then proceeded
with data preprocessing, model building & evaluation and improving the best chosen
model.
Here, built 4 models using Machine Learning (Logistic Regression & Random Forest),
Anomaly Detection (Isolation Forest) & Deep Learning (Neural Network (MLP –Multi layer
Perceptron)), and evaluated their performance using different Evaluation Matrices
(Classification Report , ROC - AUC score & curve and Precision - Recall Curve)
After comparison, Random Forest emerged as the optimal choice according to the
problem statement as we can choose a model prioritizing high fraud detection while
tolerating some false positives.
To further enhance results, an ensemble model combining Random Forest with Isolation
Forest was implemented, Leveraging the strengths of both models, Random Forest
maintains good performance across classes, while Isolation Forest excels at identifying
outliers (potentially fraudulent transactions)..
Overall, this project showcases the effectiveness of various techniques in combating credit
5. EDA (EXPLORATORY DATA ANALYSIS)
Data
Cleaning Removed the
columns that are
not required for
model building
No nulls were
there & Rectified
inappropriate
datatype
Feature
Engineering
Created Some new
features as
required
•For e.g., is_fraud_cat
for categorical analysis,
•for numerical analysis
age' , 'trans_month',
'trans_year',
'month_name’,etc.
Categorical
Variable
Analysis
Visualized -
•Transaction categories
and gender
distribution, both for
the entire dataset and
specifically for
fraudulent transactions.
•Top 10 fraudulent
transactions by job,
city, and state
Numerical
Variable
Analysis
Visualized Overall
Skewness
Class balance –
•Not Fraud (99.4%)
•Fraud (0.6%)
Bivariate Analysis -
Vizualisation with
'is_fraud'
•age groups ,
•latitudinal &
longitudinal distance
and
•month & year.
6. • There are no missing values (nulls) in dataset,
• but some data types need correction.
Data
Quality:
•Shopping_net and grocery_pos categories have the highest number of fraudulent
transactions, despite gas_transport having the most overall transactions.
•Gender distribution is nearly balanced for both overall and fraudulent transactions.
•Top fraudulent transaction jobs include materials engineer, trading standards
officer, and naval architect. Cities with the most fraud are Houston, Warren, and
Huntsville. States with the most fraud are NY, TX, and PA.
Categorical
Variables:
•The dataset is imbalanced, with a very small percentage of fraudulent transactions
compared to non-fraudulent ones.
•Age group 20-40 seems to be more targeted by fraudsters. There's a potential
location component to the fraud, with more cases closer to the equator and eastern
hemisphere.
•Most frauds occur in March, May, and February. 2019 has significantly more fraud
cases compared to 2020.
Numerical
Variables:
KEY FINDINGS OF EDA :
7. DATA PREPROCESSING
converted categorical
into numerical variables-
•Binary Encoding : Gender
•One Hot Encoding :
Transaction Category
Encoding
Performed standard
scaling to normalize
numerical features.
Ensures all variables are
on a similar scale,
preventing features with
larger magnitudes from
dominating the model.
Standard Scaling:
To handle imbalance of
the dataset.
Adding more copies of
the minority class to
balance the dataset.
SMOTE (Synthetic
Minority Over-sampling
Technique) -
•a smarter way to oversample,
it creates synthetic samples
that are similar to the existing
minority class samples.
Oversampling
8. ALGORITHM USED FOR MODEL BUILDING
Machine Learning Technique
• Logistic Regression:
• Interpretability: Provides straightforward interpretations of coefficients for
understanding feature impact on fraud likelihood.
• Simplicity: Easy implementation and understanding facilitate communication with
stakeholders.
• Random Forest:
• Complex Relationship Capture: Excels at capturing complex data relationships to
detect subtle fraud patterns.
• Minimal Feature Engineering: Requires minimal feature manipulation, suitable for
challenging feature selection scenarios.
Anomaly Detection Technique
• Isolation Forest:
• Efficient Anomaly Detection: Efficiently isolates anomalies (fraudulent transactions) in
high-dimensional data.
• Distribution Agnostic: Robust against various fraud patterns without assuming
specific data distributions.
Deep Learning Technique
• Neural Network (MLP Classifier):
• Nonlinear Pattern Detection: Captures nonlinear data relationships for sophisticated
fraud detection.
• Scalability: Handles large data volumes and adapts to real-time fraud detection needs.
9. EVALUATION MATRIX USED
Classification
Report
•Precision: The
proportion of correctly
predicted instances of a
class out of all instances
predicted as that class
•Recall : The proportion
of correctly predicted
instances of a class out
of all instances that truly
belong to that class.
•F1- score : It is a
combination of
precision and recall into
a single value. It gives
you a balanced measure
of how well model is
performing.
•Accuracy : the
proportion of correctly
classified instances out
of the total instances.
ROC-AUC
Score:
• Receiver
Operating
Characteristic
(ROC) Area
Under Curve
(AUC): A
measure of
the classifier's
ability to
distinguish
between
classes. A
higher AUC
indicates
better
classifier
performance. ROC-AUC
Curve:
• Graphical
representatio
n of the true
positive rate
(recall)
against the
false positive
rate at
various
threshold
settings. It
illustrates the
trade-off
between true
positive rate
and false
positive rate.
Precision-Recall
Curve
(PR
Curve):
• Graphical
representati
on of the
trade-off
between
precision
and recall for
different
threshold
settings. It
helps
evaluate
classifier
performance
when classes
10. LOGISTIC REGRESSION EVALUATION AND
INFERENCES
Inferences :
• This model achieves an accuracy of 89%, with high precision (1.00) for non-fraudulent
transactions but low precision (0.04) for fraudulent ones.
• It exhibits high recall (0.76) for fraud, but lower recall (0.89) for non-fraud cases, indicating
some missed normal transactions.
• The F1-scores are 0.94 for non-fraud and 0.07 for fraud, suggesting a significant imbalance
between precision and recall for fraudulent transactions.
• The ROC-AUC score is 0.9088, indicating good discriminative ability between fraudulent and
normal transactions.
• ROC-AUC curve displays good separation between TPR and FPR.
• The PR curve shows prioritization of capturing fraud (high recall) at the expense of
misclassifying normal transactions (low precision).
Overall, the model performs well in identifying fraud but misclassify normal transactions.
What does Logistic regression do ?
It creates a linear decision boundary by fitting a logistic function to the input features,
separating the data into two classes. It calculates the probability of a data point belonging to a
certain class based on its features.
Evaluation :
11. RANDOM FOREST EVALUATION AND INFERENCES
Inferences :
• Achieves a perfect accuracy (1.00), indicating it classified all transactions correctly (might be
due to overfitting on the training data).
• Both precision and recall are high for both fraudulent and non-fraudulent transactions.
• F1-scores are also high for both classes.
• ROC-AUC score (0.9930) suggests excellent discriminative ability between classes.
• ROC Curve: Close to top-left corner, indicating good TPR-FPR trade-off.
• Precision-Recall Curve: Fairly close to top-left corner, indicating good precision-recall
balance.
However, the perfect accuracy on the test data raises concerns about potential overfitting and
the model's ability to generalize to unseen data.
What does Random Forest do ?
It constructs multiple decision trees using bootstrapped samples of the dataset and randomly selected
subsets of features. Each tree "votes" on the class of an input, and the final prediction is determined by the
most common class among all trees. This ensemble approach helps capture complex relationships in the
data.
Evaluation :
12. ISOLATION FOREST EVALUATION AND INFERENCES
Inferences :
• Achieves high accuracy (0.97) but with a significant imbalance in precision and recall.
• Very high precision (0.99) for non-fraudulent transactions but extremely low
precision (0.01) for fraudulent ones.
• Recall is also high for non-fraud (0.97) but very low for fraud (0.03).
• F1-score reflects the imbalance (0.98 for non-fraud, 0.01 for fraud).
• Doesn't have probability prediction capability, so ROC curve cannot be plotted.
• Precision-Recall Curve: PR curve not close to top-left corner, indicating poor
performance.
While it identifies most normal transactions correctly, it struggles to detect fraudulent
What does Isolation Forest do ?
It isolates anomalies by recursively partitioning the data into subsets. It randomly selects a feature and a
split value, aiming to isolate outliers quickly. Anomalies are identified as instances that require fewer
partitions to isolate, as they are different from the majority of the data.
Evaluation :
13. NEURAL NETWORK EVALUATION AND INFERENCES
Inferences :
• Achieves high accuracy (0.98) similar to Logistic Regression.
• High precision (1.00) for non-fraudulent transactions but lower than Logistic Regression for
fraud (0.20).
• Recall is high for fraud (0.89) but lower than Random Forest.
• F1-score highlights the class imbalance (0.99 for non-fraud, 0.32 for fraud).
• ROC-AUC score (0.9919) indicates good discriminative ability.
• ROC Curve: Close to top-left corner, confirming good performance.
• Precision-Recall Curve: Reasonably close to top-left corner, suggesting good precision-
recall trade-off.
What does Neural Network (MLP Classifier) do ?
It consist of layers of interconnected neurons that process input data. In the case of MLP Classifier, multiple
layers of neurons process the input through nonlinear activation functions. These layers learn to represent
the data in a hierarchical manner, capturing intricate patterns and relationships. The network adjusts its
weights through backpropagation, minimizing prediction errors during training.
Evaluation :
14. MODELS COMPARISON
Selecting Best Model
Considering the importance of maximizing
fraud detection while tolerating some false
positives, Random Forest emerges as a
promising choice.
Overall Conclusion
• All models achieved high overall
accuracy, but Random Forest and MLP
might be overfitting on the training
data.
• Logistic Regression and MLP struggle
with precision for fraudulent
transactions, while Random Forest
offers a more balanced approach.
• Isolation Forest excels at identifying
normal transactions but fails to capture
most fraudulent ones.
Hence, Best Model out of these 4:
Random Forest
15. ENSEMBLE METHOD - RANDOM FOREST & ISOLATION FOREST
Considering that there might be overfitting in Random Forest,
Combining Random Forest and Isolation Forest –
• Random Forest maintains good performance in fraud detection and normal transaction
classification.
• Isolation Forest excels at identifying outliers, potentially fraudulent transactions, that
Random Forest might miss.
By combining them, a wider range of fraudulent activities can be captured.
Evaluation:
Final Classification Report (Random Forest + Isolation Forest):
• Achieves an accuracy of 0.97, indicating less overfitting
compared to Random Forest alone.
• Lower precision (0.15) for fraudulent transactions but higher
recall (0.80) compared to Random Forest. This means it might
miss some fraudulent transactions but captures more overall.
Inferences:
• The ensemble method shows promising results, achieving high
accuracy and improved recall for fraudulent transactions.
• By leveraging the strengths of both Random Forest and Isolation
Forest, a more comprehensive fraud detection system is
established.
16. CONCLUSION
While Random Forest performs well on its own, the Ensemble Method (Random Forest
+ Isolation Forest) seems to be a better choice for credit card fraud detection in this case
as it offers:
• Reduced Overfitting Risk
• Improved Fraud Detection
This analysis explored various machine learning models for credit card fraud detection.
The ensemble method combining Random Forest and Isolation Forest emerged as the
most promising choice due to its balanced performance, reduced overfitting risk,
and improved fraud detection capabilities.
GitHub Link:
For further details and access to the project code, visit my GitHub
repository:
Project_Fraud_Detection.ipynb
17. REAL-TIME IMPLEMENTATION CHALLENGES
.
Model Interpretability:
•Explanation of model
decisions is crucial for
compliance.
•Complex models may lack
interpretability.
Computational
Efficiency:
•Real-time systems require
fast inference.
•Complex models may cause
latency issues.
Handling Concept
Drift:
•Fraud patterns change over
time, leading to concept drift.
•Models must adapt to
maintain effectiveness.
Challenge
s Model Explainability:
•Use interpretable models
alongside complex ones.
•Implement techniques like
SHAP values.
Computational
Optimization:
•Optimize model architecture
and feature engineering.
•Use model compression
techniques.
Consideratio
ns
Real-time
implementation of
fraud detection models
poses challenges
related to
interpretability,
computational
efficiency, and concept
drift. By addressing
these challenges and
considering the
aforementioned
considerations,
organizations can
deploy effective fraud
detection systems in
real-time payment
processing
environments
Conclusion