Credit Scoring in Microfinance Using Non-traditional Data

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

Ruiz, S (Author) Other; Gomes, P (Author) Other; Rodrigues, L (Author) Other; João Gama (Author) FEP

Conference proceedings International

Title: EPIA

Pages: 447-458

18th EPIA Conference on Artificial Intelligence, EPIA 2017

5 September 2017 through 8 September 2017

Indexing

ISI Web of Knowledge - 10 Citations

Scopus - 15 Citations

Other information

Authenticus ID: P-00M-YHV

DOI: 10.1007/978-3-319-65340-2_37

Abstract (EN): 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 paper 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, loans granted through the models criteria presented fewer overdues, also the approval criteria of the models increased the amount of granted loans substantially. 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 paper shows that credit scoring can be useful in emerging markets. The non-traditional data can be used to build algorithms that can identify good borrowers as in traditional banking. © Springer International Publishing AG 2017.

Language: English

Type (Professor's evaluation): Scientific