Suspicious Behavior Detection in Debit Card Transactions using Data Mining

Abstract

The approach used in this paper is an implementation of a data mining process against real-life transactions of debit cards with the aim of detecting suspicious behavior. The framework designed for this purpose has been obtained through merging supervised and unsupervised models. First, due to unlabeled data, Twostep and Self-Organizing Map algorithms have been used in clustering the transactions. A C5.0 classification algorithm has been applied to evaluate supervised models and also to detect suspicious behaviors. An innovative plan has been designed to evaluate hybrid models and select the most appropriate model for the solution of the fraud detection problem. The evaluation of the models and the final analysis of the data took place in four stages. The appropriate hybrid model was selected from among 16 models. The results show a high ability of selected model in detecting suspicious behavior in transactions involving debit cards.

Advancing the assessment of automated deception detection systems: Incorporating base rate and cost into system evaluation

“…Much of this research focuses on detection. For example, researchers have studied malware detection (Kolter & Maloof, ; Souri & Hosseini, ), network intrusion detection (Shah, Qian, Kumar, Ali, & Alvi, ; Zhu, Premkumar, Zhang, & Chu, ), suspicious debit card transactions (Saghehei & Memariani, ), insider threat detection (Legg, Buckley, Goldsmith, & Creese, ), and phishing detection (Thabtah & Abdelhamid, ) using analytics methods. Many of these are low base rate problems, or problems in which the class of interest makes up only a small portion of the overall sample.…”