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September 10, 2025

Enhancing Scalability and Transparency in AI-Driven Credit Scoring: Optimizing Explainability for Large-Scale Financial Systems

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Authors

DTDaniel ThomasGeorgetown University

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Implication

Exploratory analysis reveals enhanced transparency and fairness in AI credit scoring, suggesting improved regulatory compliance.

Key Points

  • Enhancing explainability improves trust in AI-driven credit scoring methods.
  • Findings suggest that performance optimization techniques can maintain scalability in credit scoring systems.
  • Data from 2.3 million loan applications was used to investigate challenges in AI transparency.
  • This research highlights the necessity for interpretable AI systems in financial decision-making.

Cite This Study

Daniel Thomas (2025) studied this question.

synapsesocial.com/papers/68c1c9dd54b1d3bfb60f30b7https://doi.org/10.31224/5023
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Explainable Artificial Intelligence for Credit Risk Assessment: Balancing Transparency and Predictive Performance2025 · 3 citations
  2. 2EXPLAINABLE AI IN MICROCREDIT RISK ASSESSMENT: BALANCING ACCURACY AND TRANSPARENCY2026
  3. 3Credit Risk Modeling with Explainable AI: Predictive Approaches for Loan Default Reduction in Financial Institutions2021 · 17 citations
  4. 4Enhancing ML Models Interpretability for Credit Scoring2025
  5. 5Explainable Artificial Intelligence Credit Risk Assessment using Machine Learning2025