BEHAVIORAL PROFILING FOR CARD-NOT-PRESENT FRAUD DETECTION LEVERAGING ISO8583 DATA TO IDENTIFY ANOMALOUS PATTERNS

Card-Not-Present (CNP) fraud continues to rise, with fraudsters exploiting sensitive cardholder data to execute unauthorized transactions. This paper presents a behavioral profiling framework that uses ISO8583 fields to identify transaction anomalies indicative of fraudulent activity. By analyzing fields such as transaction amounts, merchant categories, POS entry modes, and terminal identifiers, the framework establishes behavioral baselines for individual cardholders and aggregates patterns across similar cardholder pro-files. Fraudulent behaviors, such as testing cards with small transactions before escalating to larger amounts, are detected by monitoring deviations from typical spending patterns. These deviations are flagged as anomalies, enabling early detection and prevention of fraudulent activities. The proposed framework also considers shared behavioral insights across multiple cardholders to enhance detection accuracy while minimizing false positives. A prototype implementation demonstrates the practical applicability of this approach, offering a scalable and efficient solution for CNP fraud detection using ISO8583 data. By focusing on behavioral profiling, this work bridges the gap between traditional rule-based systems and adaptive, data-driven fraud prevention methods.