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This research aims to develop a framework for integrating AI into creditworthiness analytics while meeting regulatory requirements.

May 29, 2026Open Access

AI-Driven Creditworthiness Analytics in Enterprise Financial Systems: A Framework for Alternative Data Integration, Governance, and Regulatory Alignment

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Framework integrates alternative data in credit risk decisioning, ensuring governance and regulatory alignment.

This research aims to develop a framework for integrating AI into creditworthiness analytics while meeting regulatory requirements.
Presents a practitioner-developed framework targeting architectural and design challenges in financial decisioning systems.
Evaluates performance using evidence from real-world deployment with over 1.5 million credit decisions annually.
Assesses Gini coefficient and compliance with regulatory standards.
Achieved a Gini coefficient of 0.74 on hold-out samples indicating effective predictive accuracy.
Reduced time-to-production for model updates by 66%.
Achieved an 87% reduction in adverse regulatory findings during examinations.

Mesbaul Haque Sazu (2026) studied this question.

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