Enhancing Credit Card Fraud Detection with Regularized Generalized Linear Models: A Comparative Analysis of Down-Sampling and Up-Sampling Techniques

Authors : Cyril Neba C.; Gerard Shu F.; Adrian Neba F.; Aderonke Adebisi; P. Kibet.; F.Webnda; Philip Amouda A.

Volume/Issue : Volume 8 - 2023, Issue 9 - September

Google Scholar : https://bit.ly/3TmGbDi

Scribd : https://tinyurl.com/44dh7vkw

DOI : https://doi.org/10.5281/zenodo.8413849

This study highlights the problem of credit card fraud and the use of regularized generalized linear models (GLMs) to detect fraud. GLMs are flexible statistical frameworks that model the relationship between a response variable and a set of predictor variables. Regularization Techniques such as ridge regression, lasso regression, and Elasticnet can help mitigate overfitting, resulting in a more parsimonious and interpretable model. The study used a credit card transaction dataset from September 2013, which included 492 fraud cases out of 284,807 transactions.The rising prevalence of credit card fraud has led to the development of sophisticated detection methods, with machine learning playing a pivotal role. In this study, we employed three machine learning models: Ridge Regression, Elasticnet Regression, and Lasso Regression, to detect credit card fraud using both Down-Sampling and Up-Sampling techniques. The results indicate that all three models exhibit accuracy in credit card fraud detection.Ridge Regression consistently outperforms the others in both Down-Sampling and Up- Sampling scenarios, making it a valuable tool for financial institutions to safeguard against credit card fraud threats in the United States.

Keywords : Machine Learning, Credit Card Transaction Fraud Detection, Regularized GLM, Ridge Regression,


Paper Submission Last Date
31 - December - 2023

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