Credit-worthiness Prediction in Microfinance using Mobile Data Thirty Seventh International Conference on Information Systems, Dublin 2016 1 Credit-worthiness Prediction in Microfinance using Mobile Data: A Spatio- Network Approach Research-in-Progress Tianhui Tan Department of Information Systems, National University of Singapore 13 Computing Drive, Singapore 117417 tianhui.tan@u.nus.edu Prasanta Bhattacharya Department of Information Systems, National University of Singapore 13 Computing Drive, Singapore 117417 prasanta@comp.nus.edu.sg Tuan Q. Phan Department of Information Systems, National University of Singapore 13 Computing Drive, Singapore 117417 disptq@nus.edu.sg Abstract Many communities in underdeveloped and developing economies of the world suffer from lack of access to personal credit via formal financial institutions, like banks. However, with the rapid increase in Internet and mobile phone penetration rates, firms are now trying to circumvent this problem using novel technology-enabled approaches. In this research, we leverage a real-world dataset obtained in collaboration with a microfinance firm to show that locational data from mobile phones, coupled with information about communication networks, can be effectively exploited to improve prediction of loan default rates. Specifically, we draw upon recent work in network cohesion based regression modeling to develop a model that uses locational predictors, but within a networked context. We contend that the results from our research can not only illuminate how locational data might be used in assessing creditworthiness, but also empower microfinance firms in resource-poor communities with novel methods for credit scoring. Keywords: Microfinance, Credit scoring, Locational data, Network cohesion, Logistic regression Introduction Individuals in developing and underdeveloped countries often lack access to basic financial services. For instance, a recent report suggests that up to 2 billion people in the world still do not have a bank account, despite a 20% drop in this number (Financial Exclusion 2015). One key consequence of this exclusion is the difficulty in applying for personal loans or credit. Since payment institutions have no history of the applicant’s creditworthiness, nor any personal information about their current social and financial status, most people in resource-poor communities have to look for alternate avenues (e.g. informal lending networks) to secure financial credit. There have been some recent advances at alleviating this problem