How far can we go? Determining the optimal loan size in progressive lending

Dhib, Nahla; Ashta, Arvind

doi:10.1002/jsc.2432

Cited by 3 publications

(2 citation statements)

References 53 publications

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“…The analysis of credit dynamics can be extended even to such novel areas as crowdlending (Krishnan et al, 2021). Furthermore, the trends in lending might impact the activity of microfinance institutions as well (Dhib & Ashta, 2021). Mumtaz et al (2018) found that credit supply shocks, which increased interest rate spreads by 10 basis points, reduced GDP growth and inflation by 1% after 1 year.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

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Accelerate or brake?—The connection of credit gaps and economic disequilibrium

Fazekas,

Póta,

Becsky‐Nagy

2024

Strategic Change

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Credit supply shocks significantly aggravate the impact of economic recessions. Understanding the underlying reasons why credit market shocks occur and detecting the market turbulences in time can give a decision support tool for the economic policy to intervene more efficiently on the market and to reduce the effect of economic downturns. The goal of the current article is to investigate the factors that signal possible credit shocks by analyzing the quarterly data of 17 European countries over the period 1995–2021. The focus of the article is on the credit given to the nonfinancial corporations. In our analysis, we have built on the credit gap methodology by determining the deviations of the lending activity from its trend, and then we have modeled these credit gaps using fundamental macroeconomic indicators, such as the balance of the current account, the ratio of short‐ and long‐term capital investments and government debt. Our conclusion is that in the presence of fundamental disequilibrium in the economy excessive lending creates positive credit gaps that increase the chance of negative credit market shocks. According to our new findings, the existing credit gap methodology can be improved by incorporating real economic factors; slowing long‐term capital investment and increasing the deficit of the current account signal the emergence of a credit gap. We found significant regional heterogeneity as in Southern European countries, and the credit gaps were more substantial. Among the investigated macroeconomic factors, the governmental indebtedness was especially high in these countries suggesting that countries with high public debt rates are more prone to develop credit gaps.

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Section: Theoretical Frameworkmentioning

confidence: 99%

Section: Theoretical Frameworkmentioning

confidence: 99%

Accelerate or brake?—The connection of credit gaps and economic disequilibrium

Fazekas,

Póta,

Becsky‐Nagy

2024

Strategic Change

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How can blockchain-based lending platforms support microcredit activities in developing countries? An empirical validation of its opportunities and challenges

Hoque,

Kummer,

Yigitbasioglu

2024

Technological Forecasting and Social Change

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Characterizing delinquency and understanding repayment patterns in Philippine microfinance loans

Bambico,

Vergara

2024

J. Finan. Eng.

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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.

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How far can we go? Determining the optimal loan size in progressive lending

Cited by 3 publications

References 53 publications

Accelerate or brake?—The connection of credit gaps and economic disequilibrium

Accelerate or brake?—The connection of credit gaps and economic disequilibrium

How can blockchain-based lending platforms support microcredit activities in developing countries? An empirical validation of its opportunities and challenges

Characterizing delinquency and understanding repayment patterns in Philippine microfinance loans

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