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 language | English |
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Journal | International Journal of Forecasting |
Early online date | 16 Jan 2025 |
DOIs | |
Publication status | Early online date - 16 Jan 2025 |
Publications and Copyright Policy
This work is licensed under Queen’s Research Publications and Copyright Policy.Keywords
- Modeling
- predicting failure
- US credit unions
- machine learning model