https://iphopen.org/index.php/bma <p><em><strong>IPHO-Journal of Advance Research in Business Management and Accounting,<a href="https://portal.issn.org/resource/ISSN/3050-886X"> (e-ISSN 3050-886X, p-ISSN 3050-9327)</a></strong></em> are the initiative of <strong>IPHO Journal</strong>. We are an inventive and worth-driven organization in the business of publication. We offer services in <strong>double-blind</strong> <strong>peer-reviewed journals</strong> with <strong>open online accessibility</strong>. Affirming Google scholar indexing with DOI and Crossref appendage. The world of business has undergone radical and dramatic changes in the last decades that present extraordinary challenges. The scope of this journal is inviting all the papers complementing the domain of management, etc</p> en-US <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> khanaasik95@gmail.com (Aasik Hussain) loveanju52438@gmail.com (Mohabbat Husain ) Thu, 03 Sep 2026 11:22:08 +0000 OJS 3.2.1.2 http://blogs.law.harvard.edu/tech/rss 60 https://iphopen.org/index.php/bma/article/view/485 <p>Production analytics increasingly sits in the control path of consequential, often regulated decisions - which claim to pay, which applicant to approve or decline, which risk-adjustment code to submit. The model is only one component, and a technically healthy service can still be an indefensible decision-maker when data quality, feature lineage, calibrated uncertainty, explanation, human oversight, and realized outcomes are governed unevenly. This paper specifies Governed Decision-Intelligence (GDI), a framework whose unit of assurance is the decision rather than the model and whose deliverables are validation, conformance, and audit defensibility. GDI composes established instruments - the SR 11-7 model-risk lifecycle, the NIST AI Risk Management Framework, ISO/IEC management and data-quality standards, exact TreeSHAP attribution, split-conformal prediction, post-hoc calibration, and distributional drift statistics - into one testable control structure delivered as four instruments: a seven-layer reference architecture with an explicit governance spine and closed outcome loop; eight normative invariants at RFC 2119 strength with a conformance rubric; a model-risk and validation methodology; and an evidence-and-explanation model that binds every decision to the sources, versions, calibrated confidence, and deterministic explanation required to reconstruct and defend it. The decision-quality target is grounded in a construct model relating analytics use, competency, and decision quality, with testable hypotheses. The framework is portable across regulated domains and is instantiated for risk-adjustment coding, credit underwriting with adverse-action obligations, governed credit-decisioning delivery, payment-integrity analytics, and as a normative engineering standard. GDI governs how a decision is produced and certified; the run-time operation of the system that then acts on the decision is the concern of a companion production-platform architecture, with which GDI is composable.</p> MESBAUL HAQUE SAZU Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-sa/4.0 https://iphopen.org/index.php/bma/article/view/485 Fri, 20 Jan 2023 00:00:00 +0000