https://www.iphopen.org/index.php/se <p><strong>IPHO-Journal of Advance Research in Science And Engineering.<a href="https://portal.issn.org/resource/ISSN/3050-8797"><em>(e-ISSN.3050-8797, p-ISSN 3050-9270) </em></a></strong>Computer Science is the systematic study of the feasibility, structure, expression. It is one of the fastest growing career fields in modern history.Mechanical engineering is a discipline of engineering that applies the principles of engineering, physics and materials science for analysis, design,Electrical and electronics engineering is engineering branch, which focuses on the use of electricity on different forms. It is the branch which deals with the uses of biomechanics, aerodynamics, fluid mechanics, automobiles, hydraulics, infrastructure, designing, analysis of geotechnical studies</p> IPHO Journal en-US IPHO-Journal of Advance Research in Science And Engineering 3050-9270 <p>Author(s) and co-author(s) jointly and severally represent and warrant that the Article is original with the author(s) and does not infringe any copyright or violate any other right of any third parties and that the Article has not been published elsewhere. Author(s) agree to the terms that the <strong>IPHO Journal</strong> will have the full right to remove the published article on any misconduct found in the published article.</p> https://www.iphopen.org/index.php/se/article/view/459 <p>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.</p> Mesbaul Haque Sazu Copyright (c) 2026 IPHO-Journal of Advance Research in Science And Engineering https://creativecommons.org/licenses/by-nc-sa/4.0 2026-05-27 2026-05-27 4 5 01 07 10.5281/zenodo.20410547