The rise of financial technology (Fintech) motivates practitioners and researchers to explore alternative data sources and enhanced credit scoring methods for better assessment of consumers’ credit risk. In this study, we examine whether deep-level diversity derived from consumers’ multimodal social media posts (i.e., alternative data) can enhance credit risk assessment or not. First, we propose novel lifestyle-based risk constructs (e.g., opinion risk) to capture consumers’ deep-level diversity. Second, we incorporate these lifestyle-based risk constructs into econometric models to empirically evaluate the relationship between consumers’ deep-level diversity and their credit risk. Using a credit scoring dataset provided by a Fintech firm listed on Nasdaq, our econometric analysis reveals that consumers’ opinion risk constructs extracted from their multimodal social media posts are positively associated with their credit risk. Furthermore, the proposed opinion risk constructs can significantly improve the effectiveness of predicting consumers’ credit risk. Interestingly, our empirical results also show that combining the opinion risk constructs derived from images and text can significantly improve the effectiveness in credit risk prediction. This work contributes to the Fintech domain by proposing novel lifestyle-based risk constructs for decision support in the credit scoring context.
Wang, Qiping; Lau, Raymond Yiu-Keung; Ngai, Eric W. T.; Thatcher, Jason Bennett; and Xu, Wei, "Consumers’ Opinion Orientations and Their Credit Risk: An Econometric Analysis Enhanced by Multimodal Analytics" (2023). JAIS Preprints (Forthcoming). 123.
Available at: https://aisel.aisnet.org/jais_preprints/123