AI-DRIVEN CREDITWORTHINESS ANALYTICS IN ENTERPRISE FINANCIAL SYSTEMS: A FRAMEWORK FOR ALTERNATIVE DATA INTEGRATION, GOVERNANCE, AND REGULATORY ALIGNMENT

The integration of artificial intelligence into enterprise credit risk decisioning represents one of the most consequential analytical transformations in contemporary financial services. While machine learning methods have demonstrated measurable improvements in predictive accuracy over traditional scorecards, their adoption in consumer and commercial credit has been constrained by regulatory explainability requirements, data governance challenges, and the absence of practical frameworks for integrating alternative data sources into governed, production-scale decisioning systems. This paper presents a practitioner-developed framework for AI-driven creditworthiness analytics that addresses three interrelated challenges: the architectural requirements for integrating alternative data into credit risk models while satisfying regulatory data governance standards; the design of explainability infrastructure that produces deterministic, legally compliant adverse action explanations from complex ensemble models; and the establishment of performance monitoring protocols calibrated to regulatory examination expectations rather than academic model evaluation conventions. Evidence from a production deployment context handling over 1.5 million annual credit decisions demonstrates that the proposed framework achieves a Gini coefficient of 0.74 on hold-out samples, a 66% reduction in time-to-production for model updates, and an 87% reduction in regulatory examination adverse findings relative to baseline, while achieving full compliance with adverse action explanation requirements under the Equal Credit Opportunity Act and Consumer Financial Protection Bureau guidance. Cross-sector adoption evidence from five independent organizational contexts confirms framework generalizability across regulated AI deployment environments. Findings contribute to the growing literature on responsible AI in financial services by providing architectural specificity grounded in production deployment experience rather than simulated or laboratory data.