Social Media-Driven Credit Scoring: The Predictive Value of Social Structures

44 Pages Posted: 22 Sep 2018 Last revised: 10 Mar 2019

See all articles by Tianhui Tan

Tianhui Tan

National University of Singapore (NUS)

Tuan Q. Phan

National University of Singapore (NUS)

Date Written: July 30, 2018

Abstract

While emerging economies have seen an explosive growth of Internet population, these countries lack sophisticated credit scoring system or credit bureaus to predict creditworthiness of individuals. Leveraging the widespread adoption of social media and social network sites in emerging markets, microfinance institutions innovate on new credit scoring methods using novel data sources. In this paper, we propose a Bayesian method for social network-based credit scoring that helps to address network sparsity and data scarcity, common with ego-centric networks. Our empirical results suggest that by incorporating social network information, we can improve the creditworthiness prediction in microfinance. We believe that although lending to the poor without incurring high default rates is challenging, social network-based methods can be an effective approach used for developing countries that face the financial exclusion problem.

Keywords: Social Networks, Credit Scoring, Microfinance, Bayesian Method, FinTech

Suggested Citation

Tan, Tianhui and Phan, Tuan Q., Social Media-Driven Credit Scoring: The Predictive Value of Social Structures (July 30, 2018). Available at SSRN: https://ssrn.com/abstract=3217885 or http://dx.doi.org/10.2139/ssrn.3217885

Tianhui Tan (Contact Author)

National University of Singapore (NUS) ( email )

15 Computing Drive
Singapore, Singapore 117418
Singapore

Tuan Q. Phan

National University of Singapore (NUS) ( email )

1E Kent Ridge Road
NUHS Tower Block Level 7
119228

HOME PAGE: http://www.tuanqphan.us

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