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LoanMatrix: An Ensemble Machine Learning Framework for Automated Loan Approval Prediction

Author

Listed:
  • Mahesh R. Bhurake
  • Amit S. Marchande
  • Tejas V. Joshi

Abstract

Loan approval in commercial banks still relies heavily on manual credit reviews, which are slow and often inconsistent. In this work, we built LoanMatrix, a credit scoring system that uses gradient boosting models to automate loan decisions. We tested three setups: LightGBM alone, CatBoost alone, and a combined ensemble using simple probability averaging. We also added SHAP explanations so banks can understand why a loan was approved or rejected. On a dataset of 32,581 loan records, our ensemble model reached 93.40% accuracy and an F1-score of 82.23%. One key benefit of the ensemble was that it cut prediction variance in half — from 2.8% to 1.4% — making decisions more stable across different applicant groups. CIBIL score and annual income were the top factors in predictions. The whole system runs under 1 millisecond per prediction, making it practical for real-time banking use.

Suggested Citation

  • Mahesh R. Bhurake & Amit S. Marchande & Tejas V. Joshi, 2026. "LoanMatrix: An Ensemble Machine Learning Framework for Automated Loan Approval Prediction," International Journal of Scientific Research in Science and Technology, Technoscience Academy, vol. 13(3), pages 993-1002, June.
    • Handle: RePEc:etm:ijsrst:v13:y2026:i3:id:1692
    DOI: 10.32628/IJSRST26133231
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