A Survey On Machine Learning And Deep Learning Approaches For Credit Card Fraud Detection

Abstract

Digital Payment Systems Have Become A Vital Part Of Daily Life, With Credit Cards Being Widely Used For Both Online And Offline Transctions.Banks, Retailers, And Clients Have All Experienced Large Financial Losses Because Of The Sharp Increase In Credit Card Fraud Brought On By The Growing Use Of Credit Cards. The Highly Unbalanced Nature Of Transaction Data, Where Fraudulent Activities Are Rare And Frequently Concealed Among Legitimate Transactions, Makes It Difficult To Identify Fraudulent Transactions. Furthermore, Fraud Patterns Are Always Changing, Requiring Quick And Accurate Detection Techniques.Recent Developments In Deep Learning And Machine Learning Have Shown Tremendous Potential In Detecting Complex And Hidden Trends In Transaction Data. This Survey Examines Popular Methods For Detecting Credit Card Fraud, Such As Machine Learning, Deep Learning, And Hybrid Approaches. It Focuses On Techniques Like Multi-layer Perceptrons, Autoencoders, Convolutional Neural Networks, Attention Mechanisms, And Ensemble Learning Models.

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