A Smart Quantitative Financial & Assurance Framework for Improving Credit Scoring: Comparative Evidence from Egyptian and Global Banking & Capital Markets

Purpose and Design: This study develops and empirically tests a Smart Quantitative Financial & Assurance Framework (SQFAF) that integrates financial accounting analytics, quantitative modeling, and digital assurance to improve corporate credit-scoring models. Comparative evidence from Egyptian and global banking and capital markets highlights structural, informational, and governance differences affecting credit-risk assessment and model reliability. Methodology: The framework employs Bayesian and Conformal Prediction, causal inference, graph-based modeling, and fairness-constrained optimization, combined with digital assurance validation and drift diagnostics. Empirical tests use panel data (2018–2024) covering listed firms and financial institutions under IFRS 9, assessing predictive accuracy, interpretability, fairness, and auditability across jurisdictions. Findings: Integrating audited financial signals with smart assurance significantly enhances predictive reliability, fairness, and transparency of credit-scoring systems. The SQFAF reduces bias, improves capital-allocation efficiency, and strengthens confidence in digital credit analytics. Originality and Value: This is the first study to unify accounting-based quantitative modeling and audit-grade digital assurance within an international comparative context, establishing a transferable governance framework for model risk management under IFRS 9. Theoretical, Practical, Economic, and Social Implications: Theoretically, the research extends financial-assurance theory to AI-driven credit evaluation. Practically, it provides regulators and institutions with a blueprint for model validation. Economically, it supports efficient financing and systemic stability. Socially, it advances financial justice, reduces exclusion, and promotes trust and transparency in digital financial ecosystems.