Explainable bank failure prediction models: Counterfactual explanations to reduce the failure risk

[2407.11089] Explainable bank failure prediction models: Counterfactual explanations to reduce the failure risk Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2407.11089 (cs) [Submitted on 14 Jul 2024 ( v1 ), last revised 21 Jul 2024 (this version, v2)] Title: Explainable bank failure prediction models: Counterfactual explanations to reduce the failure risk Authors: Seyma Gunonu , Gizem Altun , Mustafa Cavus View a PDF of the paper titled Explainable bank failure prediction models: Counterfactual explanations to reduce the failure risk, by Seyma Gunonu and 2 other authors View PDF HTML (experimental) Abstract: The accuracy and understandability of bank failure prediction models are crucial. While interpretable models like logistic regression are favored for their explainability, complex models such as random forest, support vector machines, and deep learning offer higher predictive performance but lower explainability. These models, known as black boxes, make it difficult to derive actionable insights. To address this challenge, using counterfactual explanations is suggested. These explanations demonstrate how changes in input variables can alter the model output and suggest ways to mitigate bank failure risk. The key challenge lies in selecting the most effective method for generating useful counterfactuals, which should demonstrate validity, proximity, sparsity, and plausibility. The paper evaluates several counterfactual generation methods: WhatIf, Multi Objective, and Nearest Instance Counterfactual Explanation, and also explores resampling methods like undersampling, oversampling, SMOTE, and the cost sensitive approach to address data imbalance in bank failure prediction in the US. The results indicate that the Nearest Instance Counterfactual Explanation method yields higher quality counterfactual explanations, mainly using the cost sensitive approach. Overall, the Multi Objective Counterfactual and Nearest Instance Counterfactual Explanation methods outperform others regarding validity, proximity, and sparsity metrics, with the cost sensitive approach providing the most desirable counterfactual explanations. These findings highlight the variability in the performance of counterfactual generation methods across different balancing strategies and machine learning models, offering valuable strategies to enhance the utility of black box bank failure prediction models. Comments: 20 pages, 1 figure Subjects: Machine Learning (cs.LG) Cite as: arXiv:2407.11089 [cs.LG] (or arXiv:2407.11089v2 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2407.11089 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Computational Economics (2026) Related DOI : https://doi.org/10.1007/s10614-026-11353-4 Focus to learn more DOI(s) linking to related resources Submission history From: Mustafa Cavus [ view email ] [v1] Sun, 14 Jul 2024 15:27:27 UTC (379 KB) [v2] Sun, 21 Jul 2024 19:47:47 UTC (379 KB) Full-text links: Access Paper: View a PDF of the paper titled Explainable bank failure prediction models: Counterfactual explanations to reduce the failure risk, by Seyma Gunonu and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2024-07 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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