Mining Individuals’ Behavior Patterns from Social Media for Enhancing Online Credit Scoring

The sheer volume of user behavioral data captured on online social media provide credit agencies with unprecedented opportunities to tap into the low-cost social intelligence for conducting online credit scoring. However, traditional machine learning techniques simply cannot scale up with the large-scale social media data. The main contribution of the work reported in this paper is the development of a novel large-scale data analytics methodology that leverages readily available social media data for enhancing online credit scoring. In particular, the proposed methodology is underpinned by a parallel topic modeling method for user behavioral pattern mining. Based on real-world data crawled from Sina Weibo, our experimental results show that the proposed large-scale data analytics methodology can effectively and efficiently analyze user behavior patterns from online social media. Moreover, it outperforms traditional credit scoring methods.