FinTech adoption and credit risk dynamics: evidence from China’s a-share banking sector

Abstract The rapid emergence of financial technology (FinTech) has reshaped global banking. Its implications for credit risk in China’s fast-evolving market, however, remain important and underexplored. This study empirically investigates the relationship between FinTech adoption and bank credit risk using a panel dataset of 42 A-share listed banks in China over the period 2013 to 2022. A novel FinTech index is constructed through text mining of annual reports to capture bank-specific adoption levels. Employing ordinary least squares (OLS), system generalized method of moments (GMM), and robustness checks with alternative variables, the study finds consistent evidence that higher levels of FinTech adoption are significantly associated with lower non-performing loan (NPL) ratios, indicating improved credit risk profiles. Importantly, the analysis reveals that financial regulation plays a moderating role, with stricter regulatory environments diminishing the credit risk-reducing effect of FinTech. Furthermore, heterogeneity analysis suggests that the impact of FinTech on credit risk differs across bank types, with statistically weaker but economically meaningful variation. Among them, urban commercial banks experience a significant reduction in credit risk due to the adoption of FinTech. In contrast, joint-stock commercial banks show a significant increase in credit risk following FinTech adoption. For state-owned commercial banks and rural commercial banks, the effects are negative but statistically insignificant. The findings contribute to the evolving literature on FinTech, financial intermediation, and risk management, offering practical implications for bank managers and policymakers alike.