Microcredit risk assessment using crowdsourcing and social networks

Abstract

The task of automated risk assessment is attracting significant attention in the light of the recent microloan popularity growth. The industry requires a real time method for the timely processing of the extensive number of applicants for short-term small loans. Owing to the vast number of applications, manual verification is not a viable option. In cooperation with a microloan company in Azerbaijan, we have researched automated risk assessment using crowdsourcing. The principal concept behind this approach is the fact that a significant amount of information relating to a particular applicant can be retrieved from the social networks. The suggested approach can be divided into three parts: First, applicant information is collected on social networks such as LinkedIn and Facebook. This can only occur with the applicant's permission. Then, this data is processed using a program that extracts the relevant information segments. Finally, these information segments are evaluated using crowdsourcing. We attempted to evaluate the information segments using social networks. To that end, we automatically posted requests on the social networks regarding certain information segments and evaluated the community response by counting “likes” and “shares”. For example, we posted the status, “Do you think that a person who has worked at ABC Company is more likely to repay a loan? Please “like” this post if you agree.” From the results, we were able to estimate public opinion. Once evaluated, each information segment was then given a weight factor that was optimized using available loan-repay test data provided to us by a company. We then tested the proposed system on a set of 400 applicants. Using a second crowdsourcing approach, we were able to confirm that the resulting solution provided a 92.5% correct assessment, with 6.45% false positives and 11.11% false negatives, with an assessment duration of 24 hours.