Integrating Artificial Intelligence in Banking ERM Towards Efficient Mitigation of Contemporary Financial Risks in the Zimbabwean Banking Industry

Artificial Intelligence (AI), Enterprise Risk Management (ERM), Financial Fraud Detection, Machine Learning Algorithms, Non-Performing Loans (NPLs), Zimbabwean Banking Industry

Artificial Intelligence (AI) has increasingly become a cornerstone of banking Enterprise Risk Management (ERM Since the early 2010s, AI technologies have been progressively integrated into ERM frameworks to complement traditional risk management approaches, particularly in areas such as fraud detection, credit risk assessment, anti-money laundering, and cost optimization. Despite these advancements globally, the Zimbabwean banking industry continues to face significant challenges arising from economic volatility, financial fraud, non-performing loans, and compliance-related risks. Traditional ERM methods in Zimbabwean banks are largely reactive, rule-based, and limited in predictive capability, thereby constraining effective risk mitigation. This study addresses this gap by assessing the impact of integrating AI-driven tools into banking ERM systems to enhance the mitigation of contemporary financial risks in Zimbabwe. The novelty of this study lies in its holistic evaluation of multiple AI technologies—machine learning algorithms, AI-based graphical analytics, robotic process automation, and natural language processing—within a unified ERM framework tailored to the Zimbabwean banking context. Adopting a quantitative explanatory research design, the study employed cluster sampling across selected Zimbabwean banking institutions. The data were analysed using both parametric and non-parametric techniques, including comparative descriptive statistics and Weighted Averaging Partial Least Squares regression using IBM SPSS. The findings indicate that AI integration significantly enhances the efficiency of ERM by reducing financial fraud, lowering non-performing loan frequency, curbing money laundering activities, and minimizing operational costs. The study concludes that AI-driven ERM systems offer a sustainable pathway for strengthening risk resilience in the Zimbabwean banking industry.