Engineering Explainable Artificial Intelligence Frameworks for Risk-Based Internal Auditing across Digitally Transformed Banking Infrastructures Globally.

Abstract

Digitally transformed banking infrastructures increasingly operate through interconnected cloud platforms, APIs, algorithmic decision systems, real-time payment networks, and automated controls, fundamentally altering how operational, cyber, credit, compliance, and financial-crime risks emerge and propagate. These environments generate continuous volumes of heterogeneous data that exceed the capacity of periodic, rules-based internal auditing, accelerating adoption of artificial intelligence for risk detection and audit prioritisation. However, predictive accuracy alone is insufficient for assurance: auditors must establish why a model identifies an activity as high-risk, which variables influence its judgement, whether outputs remain reproducible, and how resulting evidence supports defensible audit conclusions. This study engineers an Explainable Artificial Intelligence Risk-Based Internal Auditing Framework integrating multi-source banking data, dynamic risk scoring, anomaly detection, control-risk mapping, explainability mechanisms, and human-in-the-loop validation. The framework translates model predictions into traceable risk drivers and evidence-linked audit priorities while incorporating data lineage, model governance, regulatory requirements, and continuous monitoring. It establishes an auditable pathway from banking transactions and control signals through AI inference and explanation to risk prioritisation, auditor validation, and assurance decisions across heterogeneous global banking infrastructures.