INTELLIGENT CREDIT CARD FRAUD DETECTION SYSTEM USING MACHINE LEARNING TECHNIQUES
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The rapid growth of digital payment systems has significantly increased the risk of credit card fraud. Financial institutions face major challenges in identifying fraudulent transactions due to the highly imbalanced nature of transaction data and the evolving strategies of fraudsters. Traditional rule-based detection systems are no longer sufficient to handle modern fraud patterns.This project presents a web-based Credit Card Fraud Detection System developed using Machine Learning techniques. The system analyzes transaction features such as Time, Amount, and anonymized variables (V1–V28) to classify transactions as legitimate or fraudulent. A supervised machine learning classification model is trained using a real-world dataset and deployed through a Flask-based web application. The system integrates an SQLite database to store transaction details and prediction results. The final application provides real-time fraud prediction with risk percentage, enabling better financial security and decision-making.The proposed system demonstrates how machine learning can be effectively integrated into a scalable web-based architecture for practical fraud detection applications.
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U.M, L. S. (2026). Intelligent Credit Card Fraud Detection System using Machine Learning Techniques. International Journal of Science, Strategic Management and Technology, 02(03). https://doi.org/10.55041/ijsmt.v2i3.216
U.M, Lakshana. "Intelligent Credit Card Fraud Detection System using Machine Learning Techniques." International Journal of Science, Strategic Management and Technology, vol. 02, no. 03, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i3.216.
U.M, Lakshana. "Intelligent Credit Card Fraud Detection System using Machine Learning Techniques." International Journal of Science, Strategic Management and Technology 02, no. 03 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i3.216.
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