International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Fraud Detection in Banking Transactions using Machine Learning |
|---|---|
| ABSTRACT | Fraud detection in banking systems has become critical with the rapid expansion of digital payments. Traditional rule-based systems fail to adapt to evolving fraud patterns, resulting in high false positives and financial losses. This paper proposes a machine learning-based fraud detection system using Logistic Regression and Random Forest classifiers. The system was trained on a publicly available credit card dataset containing 284,807 transactions with 0.172% fraud rate. Results demonstrate Random Forest achieves 99.96% accuracy, 95% precision, 78% recall, F1-score of 0.85, and MCC of 0.86. The system incorporates SHAP-based explainability and web interface for real-time assessment. Comparative analysis shows the approach outperforms existing methods, offering practical deployment capability for financial institutions. |
| AUTHOR | G M ANAND REDDY, B.HARSHA VARDHAN, C RAKESH REDDY, AMAR MR, BHARATH GOWDA K Associate Professor, Dept. of Computer Science and Engineering, RLJIT, Doddaballapur, Bangalore, India UG Student, Dept. of Computer Science and Engineering, RLJIT, Doddaballapur, Bangalore, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312162 |
| pdf/162_Fraud Detection in Banking Transactions using Machine Learning.pdf | |
| KEYWORDS | |
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