Using network features for credit scoring in microfinance

• Type: Article in International Scientific Journal
• Date: 2021

Authors

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

Journal

Title: International Journal of Data Science and Analytics

Vol. 12 No. 2

Pages: 121 -134

ISSN: 2364-415X

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 1 Citation

Other information

Authenticus ID: P-00T-JE8

DOI: 10.1007/s41060-021-00243-7

Abstract (EN): The usage of non-traditional data in credit scoring, from microfinance institutions, is very useful when trying to address the problem, very common in emerging markets, of the lack of a verifiable customers' credit history. In this context, this paper relies on data acquired from smartphones in a loan classification problem. We conduct a set of experiments concerning feature selection, strategies to deal with imbalanced datasets and algorithm choice, to define a baseline model. This model is, then, compared to others adding network features to the original ones. For that comparison, we generate a network that links a given user to its phone book contacts which are users of a given mobile application, taking into account the ethics and privacy concerns involved, and use some feature extraction techniques, such as the introduction of centrality measures and the definition of node embeddings, in order to capture certain aspects of the network's topology. Several node embedding algorithms are tested, but only Node2Vec proves to be significantly better than the baseline model, applying Friedman's post hoc tests. This node embedding algorithm outperforms all the other, representing a relative improvement, in comparison with the baseline model, of 5.74% on the mean accuracy, 7.13% on the area under the Receiver Operating Characteristic curve and 30.83% on the Kolmogorov-Smirnov statistic scores. This method, therefore, proves to be very promising when trying to discriminate between "good" and "bad" customers, in credit scoring classification problems.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 14