Explainable AI-Driven Decision Support for Strategic Risk Management in Financial Services

Abstract

The adoption of AI-powered decision support systems for managing complex financial risks is inevitable. However, the non-transparent nature of traditional AI models entails accountability and compliance burden. This study introduces a framework for strategical risk management in financial institutions using explainable artificial intelligence (XAI) by incorporating techniques from machine learning to provide clarity in decision-making and enhance stakeholder trust. The framework uses feature attribution, model interpretability, and visualization tools to make risk assessment models non-opaque and auditable. The implementation of the framework in several financial institutions showed that it was capable of predicting financial risks accurately while providing human-interpretable explanations on the primary risk drivers. It was also demonstrated through experiments that the XAI-driven decision support systems outdid traditional AI models in reliability, compliance, as well as strategic risk mitigation. This research demonstrates the paradigm shift that the explainable AI concept can offer to the field of financial risk management by providing a new means of approaching the issue while ensuring ease of industry uptake.