Summary of Explainable Bank Failure Prediction Models: Counterfactual Explanations to Reduce the Failure Risk, by Seyma Gunonu et al.
Explainable bank failure prediction models: Counterfactual explanations to reduce the failure risk
by Seyma Gunonu, Gizem Altun, Mustafa Cavus
First submitted to arxiv on: 14 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper addresses the challenge of developing interpretable bank failure prediction models by exploring counterfactual explanations. While complex models like random forest and deep learning offer high predictive performance, they are difficult to explain, making it hard to derive actionable insights. The authors suggest using counterfactuals to demonstrate how changes in input variables can alter the model output and suggest ways to mitigate bank failure risk. They evaluate several counterfactual generation methods, including WhatIf, Multi Objective, Nearest Instance Counterfactual Explanation, undersampling, oversampling, SMOTE, and the cost sensitive approach. The results indicate that the Nearest Instance Counterfactual Explanation method yields high-quality counterfactuals, particularly when using the cost sensitive approach. Overall, the study highlights the variability in performance of counterfactual generation methods across different balancing strategies and machine learning models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores ways to make complex bank failure prediction models more understandable by using “what if” explanations. These explanations show how changing certain factors could affect the model’s predictions and suggest ways to reduce the risk of bank failures. The authors test several methods for generating these explanations, including some that help balance uneven data sets. They find that one method, called Nearest Instance Counterfactual Explanation, produces the best results when combined with a specific approach to balancing the data. This study shows how different approaches can produce varying results and offers valuable strategies for improving the usefulness of complex bank failure prediction models. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Random forest
