How Can We Learn from Borrowers’ Online Behaviors? The Signal Effect of Borrowers’ Platform Involvement on Their Credit Risk

Xinyin Tang, Chong Feng, Jianping Zhu and Minna He

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Abstract: A growing number of borrowers are applying for digital credit through Internet platforms due to the integration of digital credit services the Internet. However, further empirical evidence is needed to explore how a borrower’s platform behaviors affect its credit risk. As such, our study uses signaling theory as the theoretical foundation to explore the overall effects of a borrower's platform involvement intensity on its credit risk based on a large consumer credit application dataset. The main finding shows the increase in a borrower’s involvement intensity reduces its likelihood of defaulting. We attribute it to the platform's belief that borrowers with high involvement intensity have the higher value to the platform. In addition, we examine how a borrower's involvement intensity is moderated by several factors, such as the stability of its platform involvement intensity and its credit history. Due to the importance of digital credit services in microfinance, we have provided useful implications for achieving win-win outcomes in the credit market for the stakeholders.

Date: 2022-12-01
New Economics Papers: this item is included in nep-ban, nep-mfd, nep-pay and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:qga8j

DOI: 10.31219/osf.io/qga8j

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