The Digital Auditor: Artificial Intelligence and the Future of Bank Examination

Abstract

This study examines the transformative role of artificial intelligence (AI) in modernizing auditing practices within the banking sector, applying a systematic mapping methodology to review 49 peer-reviewed studies. The analysis highlights AI’s application across key domains including risk assessment, fraud detection, anomaly identification, cybersecurity, and solvency forecasting. Advanced models such as support vector machines, gradient boosting algorithms (e.g., LightGBM), and deep learning architectures are increasingly deployed to enhance predictive accuracy in credit risk evaluation and financial distress forecasting. Similarly, robotic process automation (RPA) frameworks streamline audit cycles, reducing redundancy and freeing auditors to focus on higher-order judgment and interpretation. In fraud and anomaly detection, ensemble classifiers, isolation forests, and neural architectures enable more precise identification of irregularities by learning behavioral baselines and highlighting deviations in transaction data. Machine learning–driven cybersecurity tools further strengthen institutional defenses by forecasting threats and preempting unauthorized access. Despite these advances, several implementation barriers persist, including data privacy concerns, algorithmic opacity, and the challenge of integrating AI tools with legacy audit infrastructures. Ethical risks, particularly algorithmic bias and over-reliance on automation, also require attention. The study emphasizes the need for explainable AI (XAI) frameworks, fairness-aware learning techniques, and robust governance policies to ensure transparency and accountability. Ultimately, the findings propose a roadmap for responsible AI adoption in financial auditing, reinforcing governance standards and institutional resilience in an era of escalating digital complexity.