The role of explainable AI in promoting transparency in financial decision-making

The integration of artificial intelligence in financial systems has revolutionized decision-making processes, particularly in credit scoring and risk assessment. However, this technological advancement brings forth crucial questions about transparency and accountability. This article examines how Explainable AI (XAI) addresses these concerns by providing interpretable insights into algorithmic decisions while maintaining model performance. Through analysis of various implementation frameworks, regulatory requirements, and case studies, this article demonstrates how financial institutions are successfully balancing the need for sophisticated AI systems with demands for transparency. The article explores both model-specific and model-agnostic techniques, evaluating their effectiveness in different financial applications while considering the challenges of implementation and compliance. Furthermore, it examines the evolution of regulatory frameworks across different jurisdictions and their impact on XAI adoption, providing insights into future directions for both technical innovation and regulatory standardization.