Explainable AI for Regulatory Compliance in Financial and Healthcare Sectors: A comprehensive review
Year: 2025
Type: article
Abstract: Artificial intelligence (AI) is rapidly transforming the financial and healthcare sectors, offering innovative solutions for tasks like fraud detection, credit scoring, diagnostics, and treatment planning. The use of AI in these sectors is increasing, driven by the need for automation, improved decision-making, and enhanced efficiency. However, the "black-box" nature of many AI models poses challenges for regulatory compliance. Regulations such as GDPR's "Right to Explanation" and the FDA's AI/ML guidelines demand transparency and accountability in AI systems. Explainable AI (XAI) addresses this challenge by providing insights into the decisionmaking processes of AI models, enabling stakeholders to understand, trust, and effectively govern these technologies. This review article explores the role of XAI in achieving regulatory compliance within the financial and healthcare sectors. We examine key XAI methods, including SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and counterfactual explanations, and discuss their applications in real-world scenarios. We analyze case studies, regulatory perspectives, and the challenges and future trends of XAI in ensuring responsible and compliant AI adoption
Author: Nikhil Gupta
Institution: Indian Institute of Technology Bombay
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4408771031