Characterizing delinquency and understanding repayment patterns in Philippine microfinance loans

Maria Teresa Alexandra A. Bambico and John Paul C. Vergara ()
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Maria Teresa Alexandra A. Bambico: Department of Information Systems and Computer Science, Ateneo de Manila University, Quezon City, Philippines
John Paul C. Vergara: Department of Information Systems and Computer Science, Ateneo de Manila University, Quezon City, Philippines

International Journal of Financial Engineering (IJFE), 2024, vol. 11, issue 03, 1-35

Abstract: Credit scoring is used by institutions in managing the credit risk associated with their borrowers. In addition to the traditional variables used in credit scoring, recent developments in machine learning have allowed the inclusion of data sources, such as ongoing payment behaviors and patterns, creating more robust and dynamic models. While such studies have more commonly been done for credit card data, there is limited research in the context of microfinance. Investigating this niche, this study focuses on uncovering how delinquency can be characterized and whether delinquency states exist within microfinance loans using a dataset from a microfinance institution (MFI) in the Philippines. Further, Markov chains and transition state matrices from the dataset were used to predict repayment sequences, and factors such as borrower characteristics, loan information and repayment behaviors were used in predicting the final outcome of the loans. The findings show that Markov chains can predict repayment sequences using a transition state matrix based on the full term of the loan. This model outperformed other models created from transition state matrices based on the first 8, 12, and 17 weeks of the loan term, indicating the repayment performance in the first weeks is not representative of the full term and cannot be used to accurately predict future repayment patterns. The equivalent second-order Markov chains were also examined which resulted in better predictions generally. Meanwhile, in predicting the final outcome of the loans, random forest models outperformed decision tree models using an accuracy and modified accuracy metric for evaluation. Across all models assessed in this study, behavioral characteristics and payment patterns consistently fared higher in terms of feature importance than borrower and loan characteristics. Particular to tiering delinquency states, the combination of average days overdue and delinquency streak appeared to be fitting resulting in delinquency tiers Onset Delinquency and Significant Delinquency. The ability to predict repayments aims to provide MFIs with better oversight of their loan portfolios. With an emphasis on the early detection of delinquency, predictive models using relevant features, such as those identified in this study, may allow the implementation mitigating actions at specific time periods to improve overall repayment rates among borrowers of MFIs.

Keywords: Credit scoring; microfinance; delinquency; Markov chains; machine learning (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S2424786324430011

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