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Optimization of the System of Allocation of Overdue Loans in a Sub-Saharan Africa Microfinance Institution

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  • Andreia Araújo

    (Algoritmi Centre, University of Minho, 4800-058 Guimaraes, Portugal
    Pelican Rhythms, 4100-456 Porto, Portugal)

  • Filipe Portela

    (Algoritmi Centre, University of Minho, 4800-058 Guimaraes, Portugal)

  • Filipe Alvelos

    (Algoritmi Centre, University of Minho, 4800-058 Guimaraes, Portugal)

  • Saulo Ruiz

    (Pelican Rhythms, 4100-456 Porto, Portugal)

Abstract

In microfinance, with more loans, there is a high risk of increasing overdue loans by overloading the resources available to take actions on the repayment. So, three experiments were conducted to search for a distribution of the loans through the officers available to maximize the probability of recovery. Firstly, the relation between the loan and some characteristics of the officers was analyzed. The results were not that strong with F1 scores between 0 and 0.74, with a lot of variation in the scores of the good predictions. Secondly, the loan is classified as paid/unpaid based on what prediction could result of the analysis of the characteristics of the loan. The Support Vector Machine had potential to be a solution with a F1 score average of 0.625; however, when predicting the unpaid loans, it showed to be random with a score of 0.55. Finally, the experiment focused on segmentation of the overdue loans in different groups, from where it would be possible to know their prioritization. The visualization of three clusters in the data was clear through Principal Component Analysis. To reinforce this good visualization, the final silhouette score was 0.194, which reflects that is a model that can be trusted. This way, an implementation of clustering loans into three groups, and a respective prioritization scale would be the best strategy to organize and assign the loans to maximize recovery.

Suggested Citation

  • Andreia Araújo & Filipe Portela & Filipe Alvelos & Saulo Ruiz, 2022. "Optimization of the System of Allocation of Overdue Loans in a Sub-Saharan Africa Microfinance Institution," Future Internet, MDPI, vol. 14(6), pages 1-19, May.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:6:p:163-:d:825368
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    References listed on IDEAS

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    1. Trust R. Mpofu & Eftychia Nikolaidou, 2018. "Determinants of Credit Risk in the Banking system in Sub-Saharan Africa," School of Economics Macroeconomic Discussion Paper Series 2018-04, School of Economics, University of Cape Town.
    2. Carmen M. Reinhart & Kenneth S. Rogoff, 2011. "From Financial Crash to Debt Crisis," American Economic Review, American Economic Association, vol. 101(5), pages 1676-1706, August.
    3. Matt Flannery, 2007. "Kiva and the Birth of Person-to-Person Microfinance," Innovations: Technology, Governance, Globalization, MIT Press, vol. 2(1-2), pages 31-56, April.
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