Research Article

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

Authors

  • Safa SEN Ph.D. Student at The University of Miskolc, Miskolc, Hungary
  • Sara Almeida de Figueiredo Ph.D. Student at The University of Miskolc, Miskolc, Hungary

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.

Article information

Journal

Journal of Economics, Finance and Accounting Studies

Volume (Issue)

3 (2)

Pages

43-50

Published

2021-09-05

How to Cite

SEN, S., & Figueiredo, S. A. de. (2021). Predicting Bank Failures with Machine Learning Algorithms: A Comparison of Boosting and Cost-Sensitive Models. Journal of Economics, Finance and Accounting Studies, 3(2), 43–50. https://doi.org/10.32996/jefas.2021.3.2.5

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Keywords:

Financial crisis, Bank failure, CS-Forest, GLMBoost, XGBoost, Gradient Boosting