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Financial inclusion dataset classification in Eswatini using support vector machine and logistic regression

Author

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  • Stephen Gbenga Fashoto
  • Boluwaji A. Akinnuwesi
  • Elliot Mbunge
  • Andile S. Metfula

Abstract

Small scale enterprises grow with provision of financial inclusion (FI) schemes for entrepreneurs. This widens their capital base; hence invest more and increase employment rate. We focus on Eswatini FI scheme from 2018; applied SVM and LR to classify FI dataset; discovered degree to which small, micro and medium enterprises (SMMEs) within Eswatini access funds. FI dataset was extracted from Finscope database. We selected parameters; classified FI for Manzini, Hhohho, Lubombo, and Shiselweni in Eswatini using LR with 80% split for training; ten-fold cross-validation. Manzini has ten-fold cross-validation recall rate of 69.4% using SVM and 63.4% using LR; optimal performance of the 80% percentage split recall rate of 73% was for Manzini using SVM and 77.8% using LR. The 80% split outperforms ten-fold cross-validation. Findings reflect that Eswatini Government should pay more attention to enhance FI in Hhohho, Shiselweni and Lubombo and consider mobile money as key indicator for FI.

Suggested Citation

  • Stephen Gbenga Fashoto & Boluwaji A. Akinnuwesi & Elliot Mbunge & Andile S. Metfula, 2023. "Financial inclusion dataset classification in Eswatini using support vector machine and logistic regression," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 43(4), pages 507-527.
    • Handle: RePEc:ids:ijbisy:v:43:y:2023:i:4:p:507-527
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