Modelling a Behavioral Scoring System for Lending Loans using Twitter

Abstract

Traditionally, banks follow a risk assessment model in sanctioning loans. Risk assessment is performed by computing a credit score considering certain financial factors. This work proposes a behavioral score that can be computed from social media data. Social media covers almost all aspects of a person’s life. Integrating the credit score with the behavioral score of a person lowers the risk that comes with traditional assessment models. The behavioral score is measured by the profile score, financial attitude, and twit score. A general profile score is computed for the data fetched from Twitter. The twit score of a person is calculated by considering multiple parameters like relevance, usage, and authenticity. Additionally, to strengthen the model, a novel multi-level voting ensemble is implemented with 84% accuracy to scrutinize the financial attitude of the individuals. Pair wise comparison is used to reveal the importance of the various criteria analyzed. The behavioral score is derived by aggregating the three scores accordingly. This research work proposes fusing social media details as an added risk evaluation feature in granting loans.