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

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