Abstract
Since early 2000, Microfinance Institutions (MFI) have been using credit scoring for their risk assessment. However, one of the main problems of credit scoring in microfinance is the lack of structured financial data. To address this problem, MFI have started using non-traditional data which can be extracted from the digital footprint of their users. The non-traditional data can be used to build algorithms that can identify good borrowers as in traditional banking. This paper proposes an assembled method to evaluate the predictive power of the non-traditional method. By using the Weight of Evidence (WoE), a transformation based on the distribution within the feature, as feature transformation method, and then applying extremely randomized trees for feature selection, we were able to improve the accuracy of the credit scoring model by 20.20% when compared to the credit scoring model built with the traditional implementation of WoE. This paper shows how the assembling of WoE with different feature selection criteria can result in more robust credit scoring models in microfinance.
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References
Barnes, P.: E-commerce in emerging markets: the biggest growth opportunity (2016). http://marketrealist.com/2016/12/emerging-markets-better-demographics/. Accessed 20 Nov 2019
Biçer, I., Sevis, D., Bilgic, T.: Bayesian credit scoring model with integration of expert knowledge and customer data. In: International Conference 24th Mini EURO Conference “Continuous Optimization and Information-Based Technologies in the Financial Sector” (MEC EurOPT 2010), pp. 324–329. Vilnius Gediminas Technical University Publishing House “Technika” (2010)
Bjorkegren, D., Grissen, D.: Behavior revealed in mobile phone usage predicts loan repayment (2015)
Blanco, A., Pino-Mejías, R., Lara, J., Rayo, S.: Credit scoring models for the microfinance industry using neural networks: evidence from Peru. Expert Syst. Appl. 40(1), 356–364 (2013)
De Cnudde, S., et al.: Who cares about your Facebook friends? Credit scoring for microfinance. Technical report (2015)
Fifer Mandell, A., Strawther, M., Zhu, J.: Inventure: building credit scoring tools for the base of the pyramid (2015)
Kursa, M.B., Rudnicki, W.R., et al.: Feature selection with the Boruta package. J. Stat. Softw. 36(11), 1–13 (2010)
Madhavi, A.V.: Improving the credit scoring model of microfinance institutions by support vector machine (2014)
Poushter, J.: Financial inclusion: helping countries meet the needs of the under-banked and under-servedk (2016). http://www.pewglobal.org/2016/02/22/smartphone-ownership-and-internet-usage-continues-to-climb-in-emerging-economies/. Accessed 20 Nov 2019
Ruiz, S., Gomes, P., Rodrigues, L., Gama, J.: Credit scoring for microfinance using behavioral data in emerging markets. Intell. Data Anal. 23(6), 1355–1378 (2019)
Pedro, J.S., Proserpio, D., Oliver, N.: MobiScore: towards universal credit scoring from mobile phone data. In: Ricci, F., Bontcheva, K., Conlan, O., Lawless, S. (eds.) UMAP 2015. LNCS, vol. 9146, pp. 195–207. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20267-9_16
Schreiner, M.: A scoring model of the risk of costly arrears for loans from affiliates of women’s world banking in Colombia. Women’s World Banking (2000)
Schreiner, M.: The risk of exit for borrowers from a microlender in Bolivia. Center for Social Development, Washington University in St. Louis, gwbweb. wustl. edu/users/schreiner (1999)
Schreiner, M., et al.: A scoring model of the risk of costly arrears at a microfinance lender in Bolivia. Center for Social Development, Washington University in St. Louis, gwbweb. wustl. edu/users/schreiner (1999)
Siddiqi, N.: Credit Risk Scorecards. Devoloping and Implementing Intelligent Credit Scoring. Wiley, New Jersey (2006)
Sousa, M.R., Gama, J., Brandão, E.: A new dynamic modeling framework for credit risk assessment. Expert Syst. Appl. 45, 341–351 (2016)
Tibshirani, R.: Regression shrinkage and selection via the Lasso. J. Royal Stat. Soc. Ser. B (Methodological) 58(1), 267–288 (1996)
Van Gool, J., Verbeke, W., Sercu, P., Baesens, B.: Credit scoring for microfinance: is it worth it? Int. J. Fin. Econ. 17(2), 103–123 (2012)
Zhu, J., Hastie, T.: Classification of gene microarrays by penalized logistic regression. Biostatistics 5(3), 427–443 (2004)
Acknowledgment
This article is a result of the project Risk Assessment for Microfinance, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF)
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Ruiz, S., Gomes, P., Rodrigues, L., Gama, J. (2020). Assembled Feature Selection for Credit Scoring in Microfinance with Non-traditional Features. In: Appice, A., Tsoumakas, G., Manolopoulos, Y., Matwin, S. (eds) Discovery Science. DS 2020. Lecture Notes in Computer Science(), vol 12323. Springer, Cham. https://doi.org/10.1007/978-3-030-61527-7_14
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DOI: https://doi.org/10.1007/978-3-030-61527-7_14
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