Predictive risk assessment models in banking audits opportunities and challenges for external auditors

The increasing complexity of banking operations and the surge of financial technologies have elevated audit risks and challenged conventional assurance practices. Traditional audit approaches based on sampling, retrospective testing, and manual judgment often fail to capture the dynamic risk environments characteristic of modern banking institutions. This paper presents an empirical analysis of predictive risk assessment models across three banking sub-sectors retail, investment, and microfinance. Using simulated data reflecting 300 firm-year observations, the study evaluates how financial, operational, and governance indicators predict audit risk through logistic regression and random forest modeling. Key variables include return on assets (ROA), leverage ratio, liquidity ratio, internal control score, and board independence.

Results indicate that predictive analytics can differentiate audit risk profiles among banking types, with investment banks exhibiting the highest sensitivity to leverage and internal control weaknesses. Predictive models achieved a classification accuracy above 80%, highlighting their value for risk-based audit planning. However, challenges persist regarding data governance, explainability, and regulatory integration. The study concludes that predictive analytics can transform external audit strategy by improving early risk detection, enhancing evidence quality, and aligning with international standards such as ISA 315 (Revised) and PCAOB AS 2110. Future research should focus on integrating unstructured data and developing explainable AI models to strengthen transparency and trust in predictive audit tools.