MT #4 - AI-Powered Credit Risk Assessment: Maher Asal (HV)

Abstract

The financial industry faces mounting challenges in enhancing credit risk assessment practices, particularly in terms of transparency, fairness, and alignment with sustainability objectives. Traditional credit risk models predominantly rely on historical financial data, which often restricts adaptability in a dynamic market. Additionally, AI-based credit models, despite their promise, encounter criticism for opacity and potential biases that can lead to unfair lending practices. Moreover, there is an increasing demand to integrate Environmental, Social, and Governance (ESG) metrics into credit assessments to better reflect long-term sustainability risks.

This paper examines the potential of the AI-Driven Transparent Credit Risk Assessment Platform (AICRA) as a transformative solution to these challenges. AICRA leverages Explainable AI (XAI) to ensure interpretability in credit decisions, a real-time bias detection module to enhance fairness, and ESG risk integration to provide a dual risk assessment framework. By combining these features, AICRA aims to deliver a modern credit assessment model that aligns with regulatory expectations and the ethical standards of responsible finance. The study employs a phased methodology, encompassing platform development, pilot testing with selected financial institutions, and full rollout to a broader market. Data from pilot implementations will be analyzed to assess AICRA’s effectiveness in improving transparency, reducing bias, and incorporating ESG factors into the risk evaluation process. Expected results suggest that AICRA will enhance trust and compliance in credit assessments, giving financial institutions a competitive edge through responsible lending practices. This research contributes to the evolving field of AI in finance by offering an adaptable, transparent, and ethically grounded credit assessment framework that supports the industry's transition toward sustainable finance