Reducing Mailed-Check Fraud Using a Real-Time Hybrid AI Framework for Credit Card Payments

Rahul Gurap

Abstract: Achieving up to a 30% reduction in mailed-check fraud represents a meaningful advance in credit card payment security. Mailed check fraud leads to millions in annual losses due to delayed clearing, manual verification, and limited real-time risk visibility. While artificial intelligence (AI) is widely used for detecting electronic payment fraud, its application to mailed checks is limited. This paper proposes a real-time hybrid AI framework that combines supervised machine learning and deterministic rules to identify fraud and support funds-availability decisions in mailed credit card check processing. The framework uses behavioral, statistical, and contextual features to deliver risk scores and automate key decisions. Experiments show fewer false positives and improved detection compared to traditional rule-based systems, underscoring its practical benefits.

Keywords: Fraud Detection, Hybrid Artificial Intelligence, Risk Scoring, Mailed Check Payments, Financial Security

How to Cite?: Rahul Gurap, "Reducing Mailed-Check Fraud Using a Real-Time Hybrid AI Framework for Credit Card Payments", Volume 15 Issue 1, January 2026, International Journal of Science and Research (IJSR), Pages: 823-827, https://www.ijsr.net/getabstract.php?paperid=SR26103031755, DOI: https://dx.doi.org/10.21275/SR26103031755