Credit Scoring in Microfinance Using Non-traditional Data

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2017

Autores

Ruiz, S (Autor) Outra; Gomes, P (Autor) Outra; Rodrigues, L (Autor) Outra; João Gama (Autor) FEP

Ata de Conferência Internacional

Título: EPIA

Páginas: 447-458

18th EPIA Conference on Artificial Intelligence, EPIA 2017

5 September 2017 through 8 September 2017

Indexação

ISI Web of Knowledge - 13 Citações

Scopus - 17 Citações

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica