SHARC (SHAP for Regulatory Capital) is established as a regulator-aligned explainability layer that makes the Hybrid GPR-HS framework fully auditable and consistent with FRTB, ICAAP Pillar 2, and CCAR transparency requirements.
Abstract
The adoption of non-parametric machine learning models for regulatory capital estimation introduces a fundamental governance challenge: the inability to explain model outputs in a manner auditable by supervisory bodies. This'black box'problem remains a major barrier to the adoption of Gaussian Process Regression (GPR) and related ML architectures in ICAAP and CCAR workflows despite their predictive advantages over traditional parametric approaches. This paper addresses this barrier through SHARC (SHAP for Regulatory Capital), an explainability framework for the Hybrid GPR-HS architecture and its stress-testing extension. SHapley Additive exPlanations (SHAP), derived from cooperative game theory and satisfying the properties of Local Accuracy, Missingness, Consistency, and Efficiency, are applied to Stressed Value-at-Risk (SVaR) outputs under three macro scenarios: West Asia War, Climate Risk, and AI Bubble/Regulatory Burden. SHARC decomposes SVaR into baseline, mean-driven, and volatility-driven components, enabling transparent linkage between scenario design and capital outcomes. Two findings emerge. First, SHARC consistently links non-linear SVaR outputs to underlying scenario inputs, confirming framework fidelity and providing auditable traceability of capital drivers. Second, under stress conditions, the mean return component (directional loss magnitude) dominates the variance component (volatility baseline) in determining capital levels, with implications for capital limit-setting, position management, and hedging strategy. The results establish SHARC as a regulator-aligned explainability layer that makes the Hybrid GPR-HS framework fully auditable and consistent with FRTB, ICAAP Pillar 2, and CCAR transparency requirements.
By empirically proving that high-performance algorithms can be mathematically blind to demographic biases, this framework directly advances SDG 10 (Reduced Inequalities) and provides the accountable, feature-level justifications required for secure and sustainable financial inclusion (SDG 8).
Htet Nge Nge Ko, Aung Htoo Khine, Shadab Kalhoro et al.· Journal of Risk and Financia...· 0 citations
Machine learning models can improve insurance pricing accuracy but create challenges for actuarial transparency and model governance. This paper develops a SHAP-based governance framework for evaluating explainable machine learning in motor insurance pricing, with particular attention to ASOP 41 and ASOP 56. The framework is evaluated using the real freMTPL2freq portfolio of 677,991 French motor third-party-liability policies. A Poisson GLM and gradient-boosted Poisson model are compared using out-of-sample deviance, cross-validation, calibration, and Gini discrimination measures. The gradient-boosted model achieves a 2.2–2.4% reduction in Poisson deviance relative to the GLM and substantially higher risk-ranking discrimination (Gini 0.158 versus 0.089). SHAP analysis identifies bonus-malus level, driver age, and population density as important predictors and indicates an age-by-vehicle-power interaction. A geographic proxy-discrimination audit finds material variation in predicted risk across areas and regions, while residual association with population density is weak after controlling for conventional rating factors. The results demonstrate that SHAP can provide useful evidence for model understanding and actuarial communication, but cannot by itself establish compliance with ASOP 41 or ASOP 56. The proposed framework therefore integrates explainability with validation, calibration, stability, and fairness diagnostics to support broader actuarial model governance.
Shadrick Chanda· Journal of economics, financ...· 0 citations
An actionable explainable AI framework for credit risk that combines an eXtreme Gradient Boosting classifier with Shapley Additive Explanations (SHAP) and Diverse Counterfactual Explanations (DiCE) organized under the Situation Awareness Framework for Explainable AI (SAFE-AI).
Matheus Francelino Bezerra da Silva, Carlos Quartucci Forster· International Conference on...· 0 citations
Machine learning systems deployed for credit, hiring, and resource distribution are increasingly subject to regulatory oversight from policies such as the EU AI Act and GDPR. Current fairness governance practices rely on observational fairness metrics, post-hoc explainability, and immutable audit logs, but provide limited support for causal attribution and efficient evidentiary verification. We introduce Causal Evidentiary Governance (CEG), a framework in which regulated institutions commit to a versioned directed acyclic graph (DAG) that partitions causal pathways into allowable and disallowed groups. The Causal Harm Rate measures prediction variation attributable to disallowed causal pathways. Each decision is accompanied by a signed Decision-Evidence Packet (DEP), cryptographically binding the prediction to a digest of the published DAG and path-specific attributions. DEP digests can be appended to a Merkle tree to enable logarithmic-cost inclusion proofs. We validate CEG through a two-layer empirical methodology using demographic summaries from four years of PMA credit supervisory data to construct 10,000 synthetic credit applicants across four strategic DAG counterfactuals. Causal Harm Rate isolates injected causal effects more clearly than demographic parity or equalized odds. Cross-model validation and ablation studies assess robustness. Evaluation on the German Credit dataset shows that harm associated with specific causal pathways can be substantially understated by associational fairness metrics. Finally, a proof-of-concept implementation demonstrates operationally plausible throughput and highlights relevant performance tradeoffs.
The wide implementation of advanced Machine Learning (ML) models in digital payment systems, especially for fraud detection and credit risk assessment, has substantially improved operational efficiency and transaction security. The inherent opacity, often referred to as the black box character, of these high-performing algorithms poses considerable and mounting issues related to algorithmic fairness, stakeholder trust, and compliance with regulations. This article analyzes the growing strategic significance of Explainable Artificial Intelligence (XAI) as an important governance tool for mitigating algorithmic risk in financial services. The paper exposes how XAI, informed by Agency Theory and Institutional Theory, is not just a technical requirement but an essential institutional mechanism for ensuring regulatory accountability within frameworks like the EU AI Act, restoring public trust and identifying and alleviating systemic algorithmic bias in credit scoring and fraud risk assessment. A conceptual framework is introduced and it illustrates how XAI; using post-hoc interpretation methods such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations)- bridges the knowledge disparity between intricate AI models and various human stakeholders, including customers, fraud analysts, and regulators. This transformation shifts AI from a hypothetical institutional liability to a responsible, auditable, and governable asset within the digital payment ecosystem. The report concluded by describing key areas for forthcoming empirical research on the organizational problems associated with XAI implementation across various regulatory jurisdictions
Temitope Onibaniyi, U. Lawal· Texila international journal...· 0 citations
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