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An Efficient Machine Learning-Based Approach for Credit Card Fraud Detection and Prevention

Year: 2025

Type: article

Abstract: Credit card fraud poses a major financial risk in digital transactions. This study presents a machine learningbased fraud detection system using Logistic Regression, Decision Tree, Random Forest, and XGBoost to classify transactions as fraudulent or legitimate. The model preprocesses data, applies feature scaling, and evaluates performance using accuracy, F1-score, and ROC-AUC metrics. A fraud retrieval feature allows users to analyze suspicious transactions. This approach enhances fraud detection efficiency, reduces false positives, and improves financial security.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15407984

Author: Prof.Srushti Raut, Sahil Jamadar, Rushikesh Choudhari, Piyush Mahamuni, Rohit Pawar, Shivam Chintaman

Language: English

Topic: Imbalanced Data Classification Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W6911933995

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