The problem of occupational fraud is one of the most wide-reaching operational risk event types in the Nigerian banking system. This event type spans many departments, roles, processes and systems and causes significant financial and reputational damage to banks. As a result, fraud presents banks with a real challenge in terms of knowing where to start. One of the main aims of this paper is to use stochastic probability models to predict aggregate fraud severity and fraud frequency within the Nigerian banking sector using historical data. Another objective is to describe how banks can develop and deploy business intelligence (BI) outlier-based detection models to recognize internal fraudulent activities. As the volume of transaction data grows and the industry focuses more closely on fraud detection, BI has evolved to provide proactive, real-time insights into fraudulent behaviors and activities. We discuss the fraud analytic development process, since it is a central issue in real application domains.
- business intelligence
- predictive analytics
- operational risk
- outlier detection
Nwafor, C. N., Nwafor, O. Z., & Onalo, C. (2019). The use of business intelligence and predictive analytics in detecting and managing occupational fraud in Nigerian banks. Journal of Operational Risk, 14(3), 95-120. https://doi.org/10.21314/JOP.2019.227