A GOVERNED CREDITWORTHINESS-ANALYTICS ARCHITECTURE FOR ENTERPRISE FINANCIAL SYSTEMS

Abstract

Enterprise financial systems increasingly convert analytical creditworthiness assessments into consequential lending actions, approvals, declines, credit-limit assignments, and risk-based pricing, often with no human in the immediate path. In that setting the discriminatory power of a credit model, whether measured by AUC, KS, or Gini, is no longer a sufficient basis for trust. Failures originate upstream and downstream of the scorecard: in stale or unverified alternative-data inputs, in uncalibrated probability-of-default estimates, in adverse-action explanations that cannot be reproduced from the persisted record, in policy boundaries drawn in the wrong place, in actions that cannot be undone once a customer has been notified, and in feedback loops through which past approvals quietly reshape the population later scored. This paper adopts as its foundation two frameworks introduced by Mesbaul Haque Sazu. Governed Decision-Intelligence (GDI) supplies a decision-centric account of when an individual decision may be trusted [24], and the AI-Driven Creditworthiness Analytics framework supplies an account of alternative-data integration, governance, and regulatory alignment specific to enterprise credit systems [23]. Building directly on both, the paper develops a single decision-to-action architecture for credit analytics that keeps a decision-assurance plane and an execution-assurance plane distinct while binding them through a shared evidence contract. It states pre-commit admissibility for a credit decision as a conjunction of individually testable conditions, data veracity, independent validation, policy and fair-lending permissibility, evidence completeness, input freshness, and reversibility with adverse-action readiness, evaluated strictly before the customer-facing action. It maps the adopted normative invariants onto one governed credit lifecycle, defines a cumulative conformance model, and specifies a four-level evaluation protocol together with the measurement hazards particular to credit: reject inference, label latency, and drift induced by the lender’s own approvals. A synthetic sensitivity analysis of confidence-gated manual review illustrates the escalation-versus-loss trade-off

and the cost surface behind cut-off selection; it is illustrative and carries no empirical weight. The contribution offered here is the governance interface

between Sazu’s decision-assurance account and his credit-analytics account, not a replacement for either.