Human behavior scoring in credit card fraud detection

Imane Sadgali


Now days, the analysis of the behavior of cardholders is one of the important fields in electronic payment, for credit card fraud detection systems. The behavior study reveals the types of transactions that the cardholder is used to make. A transaction that stands out with large amounts can be an indicator of a possible attempted fraud. This kind of analysis helps to extract behavioral and transaction profile patterns that can help financial systems to better protect their customers. In this paper, we propose an intelligent ML system for rules generation. It is based on a hybrid approach using rough set theory for feature selection, fuzzy logic and association rules for rules generation. A score function is defined and computed for each transaction based on the number of rules, that make this transaction suspicious.  This score is kind of risk factor used to measure the level of awareness of the transaction and to improve a card fraud detection system in general. The behavior analysis level is a part of a whole financial fraud detection system where it is combined to intelligent classification to improve the fraud detection. In this work, we also propose an implementation of this system integrating the behavioral layer. The system results obtained are very convincing.


Fraud Detection; Behavior score; Credit Card Fraud; Transaction scoring; Rules extraction



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