Credit Card Fraud Detection Using Machine Learning

Abstract

Credit card fraud poses a significant threat to the financial industry, leading to substantial financial losses and undermining consumer trust. Traditional fraud detection methods primarily rely on transaction data and behavioral analysis, but these approaches can be limited in detecting identity-based fraud. This project proposes a hybrid model that integrates machine learning algorithms with face detection techniques to enhance credit card fraud detection accuracy. By combining transactional data analysis with biometric verification, the system verifies the authenticity of the user during high-risk transactions. Machine learning models such as Logistic Regression, Random Forest, and Support Vector Machine (SVM) are employed to identify suspicious transaction patterns, while face detection using OpenCV and deep learning is used for identity verification. This dual-layered security approach increases the reliability of fraud detection systems, reduces false positives, and provides an added layer of user authentication. The proposed system demonstrates improved performance and a higher detection rate compared to conventional methods.

Introduction

Context:

• Credit card fraud is a growing problem in digital payments, threatening financial security and customer trust.

• Traditional fraud detection methods (rule-based or historical analysis) often fail against sophisticated fraud tactics.

• Machine learning (ML) offers adaptive detection by learning complex fraud patterns.

Proposed System:

• Integrates machine learning-based transaction analysis with real-time face recognition for enhanced security.

• Two-phase approach:

  1. ML models (Random Forest, SVM, Logistic Regression) identify suspicious transactions.

  2. Face verification confirms the cardholder’s identity during flagged transactions via facial recognition (FaceNet, Dlib).

Literature Highlights:

• Various ML models, including deep learning and novel network architectures, have shown promise in detecting fraud with higher accuracy and interpretability.

• Optimized frameworks (e.g., OptDevNet) outperform traditional classifiers on benchmark datasets.

• Hybrid and advanced models reduce false positives and improve detection of evolving fraud patterns.

Methodology:

• Data preprocessing includes cleaning, normalization, and balancing (e.g., SMOTE).

• Transaction features are statistically selected.

• Models are trained and evaluated using metrics like accuracy, precision, recall, F1-score, and ROC-AUC.

• Face detection is performed using Haar Cascade and HOG feature extraction combined with SVM classification.

• When a high-risk transaction is detected, the system performs real-time facial verification to ensure identity.

Results:

• Random Forest achieved excellent fraud detection performance:

  • Accuracy: 99.4%

  • Precision: 98.7%

  • Recall: 97.8%

  • F1-Score: 98.25%

• Face recognition system:

  • Haar Cascade: 93% accuracy for face detection.

  • HOG + SVM: 95.5% accuracy for classification.

  • Combined system accuracy: 94.2%

• The combined approach reduces false positives and enhances fraud prevention, especially for identity-related fraud like card-not-present cases.

Feasibility & Scope:

• The system is technically viable using open-source tools (Python, Scikit-learn, OpenCV).

• Cost-effective with moderate computing requirements.

• Operationally suitable for integration into online banking, e-commerce, and POS systems.

• Provides a two-step verification process combining behavioral and biometric security.

Conclusion

This project successfully demonstrates the integration of machine learning-based fraud detection with biometric face verification. The dual-layered approach enhances transaction security by not only identifying suspicious behavior but also verifying the identity of the user involved in the transaction. From a technical perspective, the system leverages powerful machine learning and computer vision techniques that are both accurate and efficient. Economically, it is cost-effective due to its reliance on open-source technologies. Operationally, the solution is user-friendly and easy to integrate into existing systems. In conclusion, the combined use of credit card fraud detection and face recognition represents a significant advancement in digital transaction security. This approach has the potential to reduce financial fraud dramatically while improving user confidence and system integrity.

References

[1] ”Fatf-gafi.org - Financial Action Task Force (FATF)”, Fatf-gafi.org,2016. [Online]. Available: http://www.Fatf-gafi.org. [Accessed: 22-Dec- 2015]. [2] Fatf-gafi.org, ’credit card fraud - Financial Action Task Force (FATF)’, 2014. [Online]. Available: http://www.fatfgafi.org/faq/moneylaundering/. [Accessed: 22- Dec2015]. [3] Neo4j Graph Database, ’Neo4j, the World’s Leading Graph Database’, 2014. [Online]. Available: http://neo4j.com/. [Accessed: 22- Dec- 2015]. [4] A. C. Bahnsen, A. Stojanovic, D. Aouada, and B. Ottersten. Improving credit card fraud detection with calibrated probabilities. In SDM, 2014. [5] M. Gupta, J. Gao, C. C. Aggarwal, and J. Han. Outlier Detection for Temporal Data. Synthesis Lectures on Data Mining and Knowledge Discovery, Morgan Claypool Publishers, 2014. [6] Face Detection using OpenCV and Haar Cascades. OpenCV Documentation. https://docs.opencv.org [7] Jain, A. K., Ross, A., & Nandakumar, K. (2011). Introduction to Biometrics .Springer. ISBN: 978-0-387-77326-1

Copyright

Copyright © 2025 T. Arivanantham, Prajwal Thorat, Dipa Sanap, Onkar Thorat, Sandeep Pawar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.