A Big Bang – Big Crunch Type-2 Fuzzy Logic Based System for Fraud- Detection: Case Study Balad Bank in Sudan

Abstract

Improved fraud detection systems are vital tools for the evolution of the Sudanese banking sector where the traditional fraud detection models are incapable of overcoming the emerging, innovative and new attacks that threaten large financial institutions. Hence, there is a need for accurate and transparent techniques which can automatically detect fraud with high speed and identify its causes and common patterns. Many of the Artificial Intelligence (AI) techniques are effective and provide good predictive models. Nevertheless, they are considered as black-box models. On the other hand, the white box models are easy to understand and analyze, but result in a large number of rules, besides having many parameters in each rule. In this paper, we present a novel system based on the Big Bang–Big Crunch optimization (BB– BC) approach, which is combined with type-2 Fuzzy Logic Systems to result in a small set of short IF-Then rules for the fraud detection within the Sudanese banking sector. The proposed system uses real-world dataset from Balad Bank – Sudan, which contains 803,386 transactions with 107 fraud transactions. Hence, the positive class (frauds) rate is 0.0133% of all transactions. The experimental results demonstrate that the performance of proposed system is effective in tuning the parameters of the rule base and membership functions of the Type-2 FLSs (T2FLSs) to improve the accuracy, where the proposed T2FLSs outperformed the Type-1 FLSs (T1FLSs) counterpart, as well as each rule can be simply explainable. Therefore, this can be very helpful for the Sudanese banks to start tracking the fraud cases.