Scalable and Explainable Credit Card Fraud Detection Using ML

Abstract

Our project primarily focuses on real-world credit card fraud detection. To begin, we will collect credit card datasets to serve as the training dataset. Subsequently, we will provide user credit card queries as the testing dataset. The classification process will be carried out using the Random Forest algorithm, which will analyse both the pre-existing dataset and the newly provided user data. The ultimate goal is to optimize the accuracy of the results. Additionally, we will process specific attributes to detect fraudulent activities and present the findings using graphical model visualization. The performance of the applied techniques will be evaluated based on key metrics, including accuracy, sensitivity, specificity, and precision.

Introduction

This project addresses the growing problem of credit card fraud by developing a machine learning system that accurately detects fraudulent transactions in real time. Using Logistic Regression, a statistical classification method, the system analyzes transaction data to distinguish between legitimate and fraudulent activities. The model is trained on a balanced, anonymized dataset of credit card transactions, ensuring high accuracy and reliability.

A user-friendly web interface built with Flask allows users to input transaction details and receive instant fraud predictions along with confidence scores. The system employs popular Python libraries (NumPy, Pandas, Scikit-learn) for data processing and Matplotlib/Seaborn for visualization. A secure login module with SQLite safeguards sensitive data and restricts access.

The methodology includes data acquisition, preprocessing (balancing and scaling), feature extraction (detecting behavioral patterns like unusual amounts or timing), model training, real-time prediction, and performance evaluation using metrics such as accuracy, precision, recall, and F1-score. The system workflow incorporates data normalization, feature engineering, and continuous monitoring of model performance.

Implementation involves data collection of labeled transaction records, cleaning, feature extraction, and training Logistic Regression with techniques to handle class imbalance. Extensions include a secure web-based platform for real-time fraud detection and detailed analytics. Transfer learning and advanced feature engineering can further enhance model accuracy.

Overall, this project demonstrates how machine learning combined with web technologies can create an effective, scalable, and secure tool to combat credit card fraud, reducing financial losses and improving consumer trust.

Conclusion

This project successfully demonstrates the application of machine learning in financial security by developing an efficient Credit Card Fraud Detection system. Utilizing TensorFlow/Keras and Scikit-learn for model training, along with advanced preprocessing techniques for transaction data, the system ensures accurate, real-time fraud detection. A Flask-based web interface allows users and financial institutions to interact with the model seamlessly, while SQLite is used for secure and efficient transaction data management. The results indicate that machine learning models can significantly enhance fraud detection by identifying suspicious transactions with high precision and recall, thereby helping financial institutions reduce losses and protect customer accounts. While the current implementation delivers strong performance, future enhancements could involve incorporating real-time big data processing, deploying the model at scale with cloud infrastructure, and integrating explainable AI techniques to improve transparency and trust.

References

[1] . Richhariya and P. K. Singh, “Evaluating and emerging payment card fraud challenges and resolution,” International Journal of Computer Applications, vol. 107, no. 14, pp. 5 – 10, 2014. [2] S. Bhattacharyya, S. Jha, K. Tharakunnel, and J. C. Westland, “Data mining for credit card fraud: A comparative study,” Decision Support Systems, vol. 50, no. 3, pp. 602–613, 2011. [3] A.DalPozzolo,O.Caelen,Y.-A.LeBorgne,S.Waterschoot,andG.Bontempi, “Learned lessons in credit card fraud detection from a practitioner perspective,” Expert systems with applications, vol. 41, no. 10, pp. 4915– 4928, 2014. [4] C. Phua, D. Alahakoon, and V. Lee, “Minority report in fraud detection: classi?cation of skewed data,” ACM SIGKDD explorations newsletter, vol. 6, no. 1, pp. 50–59, 2004. [5] Z.-H. Zhou and X.-Y. Liu, “Training cost-sensitive neural networks with methods addressing the class imbalance problem,” IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 1, pp. 63–77, 2006. [6] S. Ertekin, J. Huang, and C. L. Giles, “Active learning for class imbalance problem,” The 30th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 823–824, 2007. [7] M.WasikowskiandX.-w.Chen,“Combatingthesmallsampleclassimbalance problem using feature selection,” IEEE Transactions on knowledge and data engineering, vol. 22, no. 10, pp. 1388–1400, 2010. [8] S. Wang and X. Yao, “Multiclass imbalance problems: Analysis and potential solutions,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 42, no. 4, pp. 1119–1130, 2012. [9] R. J. Bolton and D. J. Hand, “Statistical fraud detection: A review,” Statistical science, pp. 235–249, 2002. [10] D. J. Weston, D. J. Hand, N. M. Adams, and C. Whitrow, “Plastic card fraud detection using peer group analysis,” vol. 2, pp. 45–62, 2008.

Copyright

Copyright © 2025 Jayavarapu Sruthi, Kanuru Chenchala Jai Vidya, Katamgari Tirumala, Jiddu Venkata Abhilashh, Mrs. CH Swapna. 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.