This paper examines how the integration of artificial intelligence (AI) and big data affects banking operations, emphasizing the crucial role of big data in unlocking the full potential of AI. Leveraging a comprehensive dataset of over 4.5 million loans issued by a leading commercial bank in China and exploiting a policy mandate as an exogenous shock, we document significant improvements in credit rating accuracy and loan performance, particularly for SMEs. Specifically, the adoption of AI and big data reduces the rate of unclassified credit ratings by 40.1% and decreases loan default rates by 29.6%. Analyzing the bank's phased implementation, we find that integrating big data analytics substantially enhances the effectiveness of AI models. We further identify significant heterogeneity: improvements are especially pronounced for unsecured and short-term loans, borrowers with incomplete financial records, first-time borrowers, long-distance borrowers, and firms located in economically underdeveloped or linguistically diverse regions. Our findings underscore the powerful synergy between big data and AI, demonstrating their joint capability to alleviate information frictions and enhance credit allocation efficiency.

Using a proprietary bank-loan-level dataset, we find a surprising negative relation between loan spreads and minimum wage. We propose a stylized model to explain the relation: banks filter out the low-quality borrowers after the wage shocks, resulting in a separating equilibrium. Our evidence is consistent with the model’s predictions: (1) city-level and firm-level evidence shows that an increase in minimum wage is negatively associated with the likelihood of obtaining bank loans, especially for labor-intensive borrowers, (2) deal-level evidence shows that both the average default rate and loan spreads decrease when minimum wage rises, and (3) subsequently, labor intensive firms that are still able to obtain bank loans when minimum wage rises outperform their peers. Our findings suggest that as more credit resources are allocated to better quality firms and leave other firms far more behind, the existence of such credit reallocation effects can exacerbate the divergence between higher and lower quality firms induced by an increase in minimum wages.

Stationary-leverage-ratio models of modelling credit risk based on constant target leverage ratios cannot generate probabilities of default which replicate empirically observed default rates. This paper presents a structural model to address this problem. The main feature of the model is that a firm’s leverage ratio is mean-reverting to a time-dependent target leverage ratio. The time-dependent target leverage ratio reflects the firm’s intention of moving its initial target ratio toward a long-term target ratio over time. We derive a closed-form solution of the probability of default based on the model as a function of the firm value, liability and short term interest rate. The numerical results calculated from the solution with simple time-dependent functions of the target leverage ratios show that the model is capable of producing term structures of probabilities of default that are consistent with some empirical findings. This model could provide new insight for future research on corporate bond analysis and credit risk measurement.