Rating the Impact of Risks in Banking on Performance: Utilizing the Adaptive Neural Network-Based Fuzzy Inference System (ANFIS)

This study aims to rate the impact of the three major risks (credit, capital adequacy, and liquidity) on three financial performance measures (return on equity (ROE), earnings per share (EPS), and price-earnings ratio (PER)). This study stands out as one of the few in its field, and the only one focusing on banks in the Middle East and Africa, to employ the adaptive neural network-based fuzzy inference system (ANFIS) that combines neural networks and fuzzy logic systems. The significance of this study lies in its comprehensive coverage of major risks and performance variables and its application of highly technical, sophisticated, and precise AI techniques (ANFIS). The main findings indicate that credit risk, as measured by the non-performing loans (NPL) has significant impact on both ROE and EPS. Liquidity risk comes second in importance for ROE and EPS, with the loan-deposit ratio (LDR) being the dominant component. In contrast, liquidity risk is the most significant determinant of PER, followed by capital adequacy. Our results also show that CAR, LDR, and NPL are the most significant risk components of capital adequacy, liquidity, and credit risks, respectively. The study contributes to business knowledge by applying the ANFIS technique as an accurate predictor of risk rating. Future research will explore the relationship between risks and macroeconomic indicators and differences among countries.