Predicting Bank Failures with Machine Learning Algorithms: A Comparison of Boosting and Cost-Sensitive Models

Abstract

Predicting bank failures has been an essential subject in literature due to the significance of the banks for the economic prosperity of a country. Acting as an intermediary player of the economy, banks channel funds between creditors and debtors. In that matter, banks are considered the backbone of the economies; hence, it is important to create early warning systems that identify insolvent banks from solvent ones. Thus, Insolvent banks can apply for assistance and avoid bankruptcy in financially turbulent times. In this paper, we will focus on two different machine learning disciplines: Boosting and Cost-Sensitive methods to predict bank failures. Boosting methods are widely used in the literature due to their better prediction capability. However, Cost-Sensitive Forest is relatively new to the literature and originally invented to solve imbalance problems in software defect detection. Our results show that comparing to the boosting methods, Cost-Sensitive Forest particularly classifies failed banks more accurately. Thus, we suggest using the Cost-Sensitive Forest when predicting bank failures with imbalanced datasets.

Predicting customer deposits with machine learning algorithms: evidence from Tunisia

“…Bank insolvencies (Le and Viviani, 2018; Petropoulos et al. , 2020; Sen and Figueiredo, 2021; Kristof and Virag, 2022), bank performance (Abu Bakar and Tahir, 2009; Kablay and Gumbo, 2021; Ainan and Nur-E-Arefin, 2022) and bank credit worthiness (Turkson et al. , 2016; Kumar et al ., 2021; Shi et al ., 2022; Sigrist and Leuenberger, 2023) are the most important issues that have been treated in this context.…”

“…A large group of literature focusing on applying machine learning in the banking industry. Bank insolvencies (Le and Viviani, 2018;Petropoulos et al, 2020;Sen and Figueiredo, 2021;Kristof and Virag, 2022), bank performance (Abu Bakar and Tahir, 2009;Kablay and Gumbo, 2021;Ainan and Nur-E-Arefin, 2022) and bank credit worthiness (Turkson et al, 2016;Kumar et al, 2021;Shi et al, 2022;Sigrist and Leuenberger, 2023) are the most important issues that have been treated in this context. A few studies have dealt with customer deposits prediction, and Pangrahi and Patnaik (2020) tried to forecast customer behavior from a bank direct marketing survey using neural network techniques.…”