Credit Scoring in Microfinance Using Non-traditional Data

Emerging markets contain the vast majority of the world’s population. Despite the huge number of inhabitants, these markets still lack a proper finance infrastructure. One of the main difficulties felt by customers is the access to loans. This limitation arises from the fact that most customers usually lack a verifiable credit history. As such, traditional banks are unable to provide loans. This thesis proposes credit scoring modeling based on non-traditional-data, acquired from smartphones, for loan classification processes. We use Logistic Regression (LR) and Support Vector Machine (SVM) models which are the top performers in traditional banking. Then we compared the transformation of the training datasets creating boolean indicators against recoding using Weight of Evidence (WoE). Our models surpassed the performance of the manual loan application selection process, improving the approval rate and decreasing the overdue rate. Compared to the baseline, the loans approved by meeting the criteria of the SVM model, presented -196.80% overdue rate. At the same time, the approval criteria of the SVM model generated 251.53% more loans. This thesis shows that credit scoring can be useful in emerging markets. The non-traditional data can be used to build strong algorithms that are able to identify good borrowers as in traditional banking.