Modeling and predicting failure in US credit unions

Qiao Peng, Donal McKillop, Barry Quinn and Kailong Liu

International Journal of Forecasting, 2025, vol. 41, issue 3, 1237-1259

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.

Keywords: Random forest; Interpretable machine learning; Explainable AI; Failure prediction; Feature selection; Credit unions (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:41:y:2025:i:3:p:1237-1259

DOI: 10.1016/j.ijforecast.2024.12.004

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