Credit Card Fraud Detection: A Comprehensive Research and Survey Study | IJCT Volume 12 – Issue 6 | IJCT-V12I6P28
International Journal of Computer Techniques
ISSN 2394-2231
Author
SHARANYA KR , DISHA K
PALLAVI KUMARI , ZULAIKATH FAHIMA
Abstract
Credit card fraud detection is the process of using various tools, technologies, and techniques to prevent unauthorized and fraudulent transactions involving credit cards, both online and offline. The primary goal is to verify that transactions are legitimate and that the cardholder is the genuine user of the card. Modern detection systems incorporate multiple layers of security, including multi-factor authentication, 3-D Secure protocols, biometric verification, and one-time passwords to ensure user identity. Advanced machine learning models play a crucial role by analyzing transaction patterns to identify anomalies, such as unusual spending locations or rapid sequences of small transactions that signal potential fraud. These models continuously learn and adapt to new fraud tactics, going beyond traditional rule-based systems by leveraging behavioral analytics and device intelligence to detect suspicious activities effectively. Addressing challenges like imbalanced datasets, where fraudulent transactions constitute a very small percentage of total transactions, is achieved using techniques such as oversampling (SMOTE), hybrid feature selection, and ensemble models that combine multiple algorithms to increase prediction accuracy. Real-time processing and scalable cloud-native infrastructures enable fast and robust fraud detection, minimizing financial losses while maintaining a smooth customer experience. Future developments are expected to integrate biometric authentication and graph-based fraud detection to uncover complex fraud networks. Overall, credit card fraud detection now blends security measures, advanced AI analytics, and adaptive learning, making it an essential safeguard in the evolving digital payment landscape.
Keywords
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Conclusion
Credit card fraud detection remains a critical and evolving challenge in the financial sector due to increasing sophistication and frequency of fraudulent activities. This research highlights the effectiveness of integrating advanced machine learning and deep learning techniques to accurately classify transactions as fraudulent or legitimate in real time. The modular architecture proposed, incorporating data ingestion, AI-driven anomaly detection, decision-making, continuous learning, and user visualization, ensures scalable and adaptive fraud prevention.
By leveraging real-time data processing, automated risk scoring, and adaptive learning from feedback, the system can reduce false positives and improve detection accuracy. Future enhancements focusing on location-based fraud, deeper behavioral analytics, and hybrid models promise further improvements. Overall, the continued innovation in AI, collaborative intelligence, and secure infrastructure is key to bridging existing gaps and safeguarding consumers and institutions from financial losses due to fraud.
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How to Cite This Paper
SHARANYA KR , DISHA K
PALLAVI KUMARI , ZULAIKATH FAHIMA (2025). Credit Card Fraud Detection: A Comprehensive Research and Survey Study. International Journal of Computer Techniques, 12(6). ISSN: 2394-2231.
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