Modeling and predicting failure in US credit unions

Qiao (Olivia) Peng, Donal McKillop, Barry Quinn, Kailong Liu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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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.
Original languageEnglish
JournalInternational Journal of Forecasting
Early online date16 Jan 2025
DOIs
Publication statusEarly online date - 16 Jan 2025

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Keywords

  • Modeling
  • predicting failure
  • US credit unions
  • machine learning model

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