Credit scoring for microfinance using behavioral data in emerging markets

• Type: Article in International Scientific Journal
• Date: 2019

Authors

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

Journal

Title: Intelligent Data Analysis

Vol. 23

Pages: 1355-1378

ISSN: 1088-467X

Publisher: IOS PRESS

Indexing

ISI Web of Knowledge - 1 Citation

Other information

Authenticus ID: P-00R-E9C

DOI: 10.3233/ida-184239

Abstract (EN): Emerging markets contain the vast majority of the world's population. Despite the enormous 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 linear models in traditional banking. Then we compared the transformation of the training datasets creating boolean indicators against the categorization 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 a decreased overdue rate. At the same time, using the score generated by a SVM model we were able to grant more loans. This paper shows that credit scoring can be useful in emerging markets. The non-traditional data can be used to build robust algorithms that can identify good borrowers as in traditional banking.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 24