Farmers’Digital Footprints and Loan Default Risk

【Authors】

ZHENG Hairong, ZHANG Yang, HE Jing, MU Zhengshe

【WorkUnit】

ZHENG Hairong （Fujian Agriculture and Forestry University, 350002）;ZHANG Yang, HE Jing, MU Zhengshe （China Agricultural University, 100083）

【Abstract】

Information asymmetry is a critical barrier that hinders banks’ management of farmers’ loan risks and underpins their reluctance to lend to farmers, and the difficulty and high cost associated with such lending. Banks increasingly recognize the importance of “digital footprints” in risk control, and exploring the value of data and unlocking its potential has become an important part of digital risk management. Compared with traditional bank information, can digital footprints provide additional information, effectively manage credit risk, and reduce lending risks? Answering this question is essential for managing loan risk in the era of Big Data and offers a pathway for how to use digital finance to deepen rural financial services.
Based on this, we constructed a signaling game model and empirically tested it using millions of farmers’ loan records from rural commercial banks to identify the impact of digital footprints on farmers’ loan default risk. The results show that digital footprints play a signaling role in preventing and controlling loan default risk, significantly reducing farmers’ loan default risk. Furthermore, this risk-reducing effect is more pronounced for farmers with abundant hard information than for groups of farmers with abundant soft information. Mechanism analysis shows that digital footprints provide incremental information on farmers’ production, operations and other flows of hard information, which strengthens the risk-reducing effect. However, farmers’ digital footprints do not provide meaningful additional insights for traditional soft information. Moreover, in areas with a higher degree of digital transformation among banks, a more mature rural credit system, and in inclusive finance pilot zones, the risk-reducing effect of digital footprints is stronger. This study reveals the role of digital footprints in identifying and monitoring farmers’ loan default risk and provides a factual basis for better exploiting financial big data in the credit market.
This paper contributes to the existing literature in the following ways. First, it introduces the concept of farmers’ digital footprints, extending the literature on the impact of digital footprints on financial institutions’ operations from e-commerce to banking. Second, it uncovers a new mechanism by which the farmers’ digital footprints affect their loan default risk. By distinguishing hard and soft information, this paper uses millions of farmers’ loan records to analyze how the data of farmers’ digital footprints reduces the risk of loan defaults mainly by providing incremental information for hard information-rich farmers, thereby enhancing the data effectiveness. Third, this paper provides a new perspective for studying farmers’ loan default risk. While previous research has focused on traditional financial data and physical collateral, this paper expands the scope of research to the context of digital finance, identifying digital footprints as a key factor affecting the loan default risk, which provides a new factual basis for the related research on farmers’ loan risk management. 
The findings have strong practical implications. They enable banks to better segment farmer groups and fully play the complementary roles of digital footprints and banks’ traditional soft and hard information in loan risk management.

【KeyWords】

Digital Footprints, Default Risk, Signaling, Soft Information, Hard Information