Citation:

Daniel Thomas, 2026. Enhancing Scalability and Transparency in AI-Driven Credit Scoring: Optimizing Explainability for Large-Scale Financial Systems  6(2): 138-145.

Abstract:

The growing adoption of Artificial Intelligence (AI) in credit scoring has significantly enhanced predictive accuracy, but it has also raised concerns regarding transparency, fairness, and trust. The “black box” nature of many Machine Learning (ML) models used in financial decision-making can hinder understanding and accountability, particularly in high-stakes scenarios such as loan approvals. To address these challenges, it is essential to develop methods that improve the explainability and scalability of AI-driven credit scoring systems. This study aims to evaluate how explainability techniques such as SHAP and LIME scale with increasing data volume in tree-based ensemble models like XGBoost and to identify optimization strategies that balance interpretability and performance. By applying these approaches to a dataset of 2.3 million loan applications from Lending Club, the research provides insights into improving the efficiency and transparency of large-scale AI systems. The findings contribute to more transparent, fair, and efficient credit scoring models, ensuring that AI-driven decisions are both interpretable and compliant with regulatory standards.

References:

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Keywords:

Explainable Artificial Intelligence (XAI), AI-Driven Credit Scoring, Credit Risk Management, SHAP, LIME, XGBoost, Model Interpretability, Financial Transparency, Scalable AI Systems, Fairness in AI