2026· Proceedings of the 15th International Conference on Data Science, Technology and Applications· pp. 178-189· 0 citations· 15 references
TL;DR
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).
Abstract
: The increasing use of machine learning models in credit risk assessment raises concerns about transparency, trust, and regulatory compliance, as many high-performing models behave as black boxes. This paper proposes an actionable explainable AI framework for credit risk that combines an eXtreme Gradient Boosting (XGBoost) classifier with Shapley Additive Explanations (SHAP) and Diverse Counterfactual Explanations (DiCE), organized under the Situation Awareness Framework for Explainable AI (SAFE-AI). Using the public HELOC dataset, we first train and tune an XGBoost model to predict default risk, then derive global and local explanations with SHAP, and finally generate counterfactual explanations with DiCE to indicate feasible changes capable of reversing unfavorable outcomes. Results show that the proposed framework provides competitive predictive performance while enhancing interpretability and actionable decision support in credit risk assessment.
The rapid adoption of digital technologies has significantly transformed the way banks and financial institutions evaluate loan applications. Machine learning (ML) models are widely used in credit risk assessment to analyze large volumes of financial data and support faster and more reliable lending decisions. However, many of these models operate as black-box systems that provide limited explanation for loan approval or rejection outcomes. In financial environments, where decisions directly impact borrowers and institutional risk exposure, lack of transparency may reduce trust and raise concerns regarding fairness and accountability. To address these challenges, this study proposes a Transparent and Explainable Artificial Intelligence (XAI) framework for risk-aware loan approval decision support. The proposed framework integrates predictive modeling with explainability techniques such as SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), Counterfactual Explanations, and Permutation Feature Importance. These techniques provide both global insights into model behavior and clear explanations for individual loan decisions. In addition, fairness evaluation mechanisms are incorporated to detect potential bias across sensitive attributes. Experimental results demonstrate that integrating explainability improves transparency and user confidence while maintaining strong predictive performance, thereby supporting reliable and responsible AI-based loan approval systems for financial institutions.
Ch.Padma, N. Bhavani, G. Prakash et al.· 2026 4th International Confe...· 0 citations
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.
CreditR1 delivers calibrated PDs with evidence-grounded reasoning that supports internal model validation and human review that supports transferability beyond the Chinese A-share market remains an open empirical question.
The growing adoption of more sophisticated machine learning models in automated decisioning of credit risks has generated very serious issues of explainability, fairness, and consumer trust, especially when loan applications are denied. Alternative methods of explanation that are available like the use of the static reason codes and traditional counterfactual techniques tend to fail to offer realistic, practical and fair advice to the impacted applicants. In this paper, we present a third-generation counterfactual explain model, which combines structural causal modeling, diffusion-based generative learning, fairness-constrained optimization, and policy adaptability control to produce trustworthy and user-friendly credit clarifications. Actionability and real-world consistency are enforced using a structural causal model to separate mutable and immutable attributes and maintain causal relationships between financial variables. A conditional diffusion network is conditioned on approved credit profiles in order to produce several plausible counterfactual representations of applicants. Such candidates are filtered by original credit model to only keep decision-flipping examples to be valid and are optimized over a multi-objective fairness-constrained formulation that balances small feature changes, realism, and diversity, and demographic equity. Additionally, a policy adaptation module, which is based on reinforcement learning, constantly balances the explanation strategy according to the changing lending policies and regulatory issues. The causal diffusion-based framework proposed had greater counterfactual validity, realism, diversity, and fairness as compared to current gradient-based and heuristic approaches on all of the tested credit datasets.
Bhuvaneswari U, S. Muthukrishnan, Pankaj Kumar Baid· 2026 7th International Confe...· 0 citations
This paper proposes an Explainable Deep Reinforcement Learning (XDRL) framework to automate and personalize credit limit adjustments based on customer behavior, financial data, and macroeconomic indicators, and learns optimal credit limit strategies that balance risk, customer satisfaction, and profitability.
B. K, Meena Krishnan· International Journal of Int...· 0 citations