Predicting the financial performance of microfinance institutions with machine learning techniques
Abstract
Purpose
Given the growing emphasis among scholars, practitioners and policymakers on financial sustainability, this study aims to explore the applicability of machine learning techniques in predicting the financial performance of microfinance institutions (MFIs).
Design/methodology/approach
This study gathered 9,059 firm-year observations spanning from 2003 to 2018 from the World Bank's Mix Market database. To predict the financial performance of MFIs, the authors applied a range of machine learning regression approaches to both training and testing data sets. These included linear regression, partial least squares, linear regression with stepwise selection, elastic net, random forest, quantile random forest, Bayesian ridge regression, K-Nearest Neighbors and support vector regression. All models were implemented using Python.
Findings
The findings revealed the random forest model as the most suitable choice, outperforming the other models considered. The effectiveness of the random forest model varied depending on specific scenarios, particularly the balance between training and testing data set proportions. More importantly, the results identified operational self-sufficiency as the most critical factor influencing the financial performance of MFIs.
Research limitations/implications
This study leveraged machine learning on a well-defined data set to identify the factors predicting the financial performance of MFIs. These insights offer valuable guidance for MFIs aiming to predict their long-term financial sustainability. Investors and donors can also use these findings to make informed decisions when selecting their potential recipients. Furthermore, practitioners and policymakers can use these findings to identify potential financial performance vulnerabilities.
Originality/value
This study stands out by using a global data set to investigate the best model for predicting the financial performance of MFIs, a relatively scarce subject in the existing microfinance literature. Moreover, it uses advanced machine learning techniques to gain a deeper understanding of the factors affecting the financial performance of MFIs.
Keywords
Acknowledgements
The authors would like to thank the Editor in Chief (Professor Huang Zhimin), Associate Editor (Dr Davide La Torre) and two anonymous reviewers for their very helpful comments and suggestions in improving the quality and content of the article. The earlier version of the paper was presented at the Second International Postgraduate Research Symposium (INPOS), School of Management, Universiti Sains Malaysia in 2023 and Second International Student Conference on Business, Education, Economics, Accounting and Management (ISC-BEAM), Faculty of Economics, Universitas Negeri Jakarta, Indonesia, in 2024.
Citation
Ting, T., Mia, M.A., Hossain, M.I. and Wah, K.K. (2024), "Predicting the financial performance of microfinance institutions with machine learning techniques", Journal of Modelling in Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JM2-10-2023-0254
Publisher
:Emerald Publishing Limited
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