A Machine Learning Perspective on the Impact of Cybercrime on Performance in the Nigerian Banking Industry

Abstract

This study strives to establish salient relationship between cyber crime and business performance, the 4th industrial revolution (4IR or Industry 4.0) surely has Artificial intelligence (AI) embedded in it, a key subset of AI that is shaping and will further shape the marketplace is Machine learning (ML). This study has leveraged ML in establishing key performance relations in the Nigerian banking industry amidst growing impedance of cyber crime against the maximization of stakeholders and shareholders returns. By mining data from past data patterns of data gotten from the Nigerian banks annual report from 2013 to 2019 and Nigerian electronic fraud forum (NEFF) and analyzing using linear regression algorithm, our study was able to establish relationships between ; ATM card crime/ Profit After Tax (PAT), PoS crime/ Earnings Per Share (EPS), and E-commerce crime/ Profit Before Tax (PBT), providing a correlation fit between these pairs with correlation coefficient values of  0.89, 0.99 and 0.99 respectively implying a very strong positive correlation between  the pairs. This study concluded that there is a strong correlation between cyber crime and performance in the Nigeria banking industry, anchoring on fiedler’s contingency theory that there is no one best way to manage cyber crime.With innovative recommendations like robust cyber security systems which are new economic knowledge (knowledge-shift), the study predicts a re-shape of growth in the Nation’s banking industry while consolidating on technological competitive advantage.