Machine Learning-Based Approaches for Credit Card Fraud Detection - A Comprehensive Review

Aug 3, 2026

Data analytics aims to uncover hidden patterns and use them to support sound decisions across diverse situations. Credit card theft has accelerated with modern technologies, becoming a frequent target for fraud. Public credit card fraud datasets are highly imbalanced, and conventional rule-based defenses struggle against fraudsters’ evolving tactics. The paper reviews machine learning methods for real-time unsupervised anomaly detection, details key data features, and discusses experiments using a weighted product approach across multiple alternative models and evaluation metrics.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

Abstract

Introduction

Problem of biased credit card fraud datasets

Supervised and unsupervised learning approaches

Features for identifying fraudulent transactions

Challenges with imbalanced data and mitigation techniques

Recent advances: ensembles, deep learning, anomaly detection

Evaluation metrics and model assessment

Machine Learning-Based Approaches for Credit Card Fraud Detection: A Comprehensive Review Dr. K.Maithili1, Dr. T. Sathish Kumar2, Dr. A. Rengarajan3, P L Srinivasa Murthy4, K. Nagamani5 1Associate Professor ,Department ofCSE KG Reddy College of Engineering &TechnologyMoinabad, Hyderabad, Telangana-501504 Mail : [drmaithili@kgr.ac.in](drmaithili@kgr.ac.in) 2Associate Professor,Department of Computer Science and EngineeringHyderabad Institute of Technology and Management, Hyderabad . Mail : [sathisht.cse@hitam.org](sathisht.cse@hitam.org) 3 Professor, Departmetn of CSE , Jain university, [Bangalore Mail : a.rengarajan@jainuniversity.ac.in](Bangalore Mail : a.rengarajan@jainuniversity.ac.in) 4Professor, Department of Computer science and Engineering, IARE [Mail : plsrinivasamurthy@iare.ac.in](Mail : plsrinivasamurthy@iare.ac.in) 5Assistant professor, Department ofCSE, MLR Institute of Technology, Dundigal, Hyderabad. [Mail : nagamanik@mlrit.ac.in](Mail : nagamanik@mlrit.ac.in) Abstract The objective of data analytics is to discover hidden patterns and use them to guide wise judgements in a range of circumstances. Theft of credit cards has significantly grown as a result of modern technologies and has become a popular target for scam artists. Publicly available databaseson credit card fraud are very unbalanced. As more people conduct business online, Fraud involving credit cards has grown to be a serious problem for both consumers and financial establishments. standard rule-based fraud detection strategies have shown to be insufficient to combat fraudsters' ever-evolving tactics. Machine learning algorithms have thus developed into a powerful tool for real-time unsupervised learning, and anomaly detection., is then explored in detail in order to accurately identify fraudulent transactions. Furthermore, we explore the various data features utilized by machine learning algorithms, including transaction history, transaction amounts, merchant information, and geographical locations.“For people, companies, and financial institutions, A significant financial danger is credit card fraud. In order to detect theft, robust methods for machine learning must be developed. researchers can help minimize financial losses associated with fraudulent activities”. In this research we will be using weighted product method. Taken as Alternative parameters is “Fraud detection using Game theory for M1, Hybrid Approach For Fraud Detection Using Svm And Decision Tree for M2, Fraud Detection Using Som & Psofor M3, Dempster Shafer Theory Along With Bayesian Learning For Detecting Fraudfor M4, Cardwatchfor M5”. Taken as Evaluation parameters is “Sum of Squared Error, Mean Squared error, Root Mean Square error, Mean Absolute error, Root Mean Square Prediction Error, and Accuracy”. Model 1 outperformed the other 4 models when a machine learning algorithm was used to identify credit card frauds. With Weighted Product Method we are able to find the best way of detection of credit card frauds by machine learning algorithm which has been evaluated with various parameters and methodology. Keywords: “Credit Card, Fraud detection, Supervised machine learning.” 1. INTRODUCTION The public ally accessible datasets on credit card fraud are severely biased. Because additional consumers more financial organizations are accepting of digital transactions, credit card theft has become more important. traditional rule-based fraud prevention techniques detection have shown to be insufficient to match the constantly developing tactics used by fraudsters. As a result, algorithms that use machine learning have developed into an effective tool for detecting fraudulent activities in real time. abstract in this introduction. Thanks to the advancement of neural network computer programmers, the field of identification of fraud has undergone a significant shift. Such algorithms also provide encouraging solutions to the issues raised by credit card fraud. Machi

FAQs

Why are rule-based credit card fraud detection strategies often insufficient?

Fraudsters continuously adapt their tactics, and traditional rule-based techniques cannot keep pace effectively.

What role do machine learning algorithms play in this research?

Machine learning supports real-time identification of fraudulent transactions by leveraging pattern recognition and anomaly detection, including both supervised and unsupervised approaches.

What evaluation metrics are used to compare the proposed models?

The study evaluates models using metrics such as Sum of Squared Error, Mean Squared Error, Root Mean Square error, Mean Absolute error, Root Mean Square Prediction Error, and Accuracy.

Aug 3, 2026

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