Assembled Feature Selection for Credit Scoring in Microfinance with Non-traditional Features

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.