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Modeling and predicting failure in US credit unions

Author

Listed:
  • Peng, Qiao
  • McKillop, Donal
  • Quinn, Barry
  • Liu, Kailong

Abstract

This study presents a random forest (RF)-based machine learning model to predict the liquidation of US credit unions one year in advance. The model demonstrates impressive accuracy on the test set (97.9% accuracy, with 2.0% false negatives and 8.8% false positives) when utilizing all 44 factors. Simplifying the model to only the top five factors based on feature importance analysis results in a slightly lower, but still significant, accuracy on the test set (92.2% accuracy, with 7.8% false negatives and 17.6% false positives). Comparisons with seven other classification methods verify the superiority of the RF model. This study also uses the Cox proportional-hazards model and Shapley value-based approaches to interpret key feature significance and interactions. The model provides regulators and credit unions with a valuable early warning system for potential failures, enabling corrective measures or strategic mergers to ultimately protect the National Credit Union Share Insurance Fund.

Suggested Citation

  • Peng, Qiao & McKillop, Donal & Quinn, Barry & Liu, Kailong, 2025. "Modeling and predicting failure in US credit unions," International Journal of Forecasting, Elsevier, vol. 41(3), pages 1237-1259.
    • Handle: RePEc:eee:intfor:v:41:y:2025:i:3:p:1237-1259
    DOI: 10.1016/j.ijforecast.2024.12.004
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