AN ENHANCED MODEL FOR BANK FRAUD DETECTION IN NIGERIAN
Keywords:
Multi-agent, Bank Fraud, Machine Learning and Credit CardAbstract
The growth in electronic transactions has resulted in a greater demand for fast and accurate user identification and authentication. Conventional method of identification based on possession of pin and password are not all together reliable. This paper aimed at design and develop an enhanced model for bank fraud detection in Nigeria banks using data mining technique and multi- agents that combine evidence from current as well as past behaviour to determine the suspicions level of each incoming transaction. The model was designed using Object-Oriented Analysis and Design Methodology (OOADM), Multi-Agent Methodology and Machine Learning Technique respectively. The model was programmed and implemented using PHP while the database was implemented with MySQL. Test results on the new system using confusion matrix shows a significant positive impact 94% accuracy in credit card fraud detection as against 57% of accuracy by the existing system, and hence a significant improvement on overall operating efficiency. Thus, the new credit card fraud (CCF) detection system using multi-agents is compatible with other detection software but has significantly higher performance efficiency (94%). The model is therefore recommended for use by banks, financial agencies and government agencies.
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