"Forecasting nonperforming loans using machine learning" by Mohammad Abdullah, Mohammad Ashraful Ferdous Chowdhury et al.
 

Forecasting nonperforming loans using machine learning

Document Type

Article

Publication Date

11-1-2023

Abstract

Nonperforming loans play a critical role in financial institutions' overall performance and can be controlled by forecasting the probable nonperforming loans. This paper employs a series of machine learning techniques to forecast bank nonperforming loans on emerging countries' financial institutions. Using quarterly cross-sectional data of 322 banks from 15 emerging countries, this study finds that advanced machine learning-based models outperform simple linear techniques in forecasting bank nonperforming loans. Among all 14 linear and nonlinear models, the random forest model outperforms other models. It achieves a 76.10% accuracy in forecasting nonperforming loans. The result is robust in different performance metrics. The variable importance analysis reveals that bank diversification is the most critical determinant for future nonperforming loans of a bank. Additionally, this study revealed that macroeconomic factors are less prominent in predicting nonperforming loans compared with bank-specific factors.

Identifier

85152041605 (Scopus)

Publication Title

Journal of Forecasting

External Full Text Location

https://doi.org/10.1002/for.2977

e-ISSN

1099131X

ISSN

02776693

First Page

1664

Last Page

1689

Issue

7

Volume

42

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