False Positives in Indian Digital Banking Fraud Detection: Challenges, Impacts and Mitigation Strategies

There are challenges like privacy, concept drift and scalability. In the future we can expect to see the Reserve Bank of Indias Digital Payments Intelligence Platform, federated learning and advanced biometrics. 7. Advancements in Artificial Intelligence and Machine Learning combined with regulatory initiatives and global lessons can offer substantial reductions in false positives. This can lead to a customer experience and improved fraud prevention.

1.        Lingareddy, N. (2025). Enhancing Digital Payment Security: UPI Fraud Detection with Advanced Machine Learning Algorithms. IEEE Conference Publication. https://ieeexplore.ieee.org/document/11077038/ (Accuracy 98.7%, False Positive Rate 1.2%). 2.        Sethi, B., Mhatre, S., Yadav, S., Das, S., & Jadhav, V. (2025). Machine Learning-Based UPI Fraud Detection: A Comprehensive Approach Using Random Forest. Atlantis Press. (Precision 97%, strong minimization of false positives). 3.        Kumar, J. (2025). Optimized Machine Learning and Deep Learning Approaches for UPI Fraud Detection. International Journal of Science and Advanced Technology. 4.        (2026). A Convolutional Neural Network Framework for Real-Time UPI Fraud Detection. TIJER. (Accuracy 97.8%, Precision 96.4%, False Positive Rate 1.4%). 5.        (2026). AI-Based Detection and Analysis of Fraud in UPI Transactions. Mazedan. (Low FP count of 28 in confusion matrix example). 6.        (2026). UPI Fraud Detection and Prevention. ResearchGate. (95.7% detection accuracy on >5 million anonymous UPI transactions). 7.        Various authors (2025). Papers in IJERT, IJCRT, IJISRT, and IRJET on ensemble methods, Isolation Forest, LSTM, and hybrid models for UPI fraud detection emphasizing FP reduction. 8.        Reserve Bank of India (RBI). (2025–2026). Digital Payments Intelligence Platform (DPIP) – AI/ML-based real-time risk scoring and intelligence sharing to reduce false positives in digital payments. 9.        RBI & NPCI initiatives (including MuleHunter.AI pilot) for mule account detection and behavioural analytics in UPI ecosystem. 10.     State Bank of India (SBI) & ICICI Bank Pilots (2025). ML-based systems reduced false positives by ~15%, improved real-time UPI monitoring. 11.     HDFC Bank + Mastercard AI Integration. Real-time behavioural analytics for fraud detection, significantly lowering false alerts.