Abstract

Artificial Intelligence (AI) is rapidly transforming the way organizations manage risks and conduct audits. Modern businesses generate vast amounts of financial, operational, and transactional data that exceed the capabilities of traditional auditing approaches. AI-driven systems can analyze these large datasets, identify hidden patterns, detect anomalies, and support timely decision-making. However, many advanced AI models operate as “black boxes,” making it difficult for auditors, managers, regulators, and stakeholders to understand how decisions are reached. This lack of transparency can reduce trust and create challenges in regulatory compliance and accountability. Explainable Artificial Intelligence (XAI) has emerged as a promising solution by providing clear and interpretable explanations for AI-generated outcomes. This mini review explores the growing role of XAI in risk management and auditing, examines key explainability techniques, discusses practical applications, and highlights current challenges and future opportunities. The review suggests that integrating XAI into auditing and risk assessment processes can strengthen organizational governance, improve decision quality, and enhance stakeholder confidence in AI-enabled systems.

Keywords: Explainable artificial intelligence; Auditing; Risk management; Transparency; Fraud detection