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.

We conceptually identify and empirically verify the features distinguishing FinTech platforms from non-financial platforms using marketplace lending data. Specifically, we highlight three key features: (i) Long-term contracts introducing default risk at both the individual and platform levels; (ii) Lenders’ investment diversification to mitigate individual default risk; (iii) Platform-level default risk leading to greater asymmetric user stickiness and rendering platform-level cross-side network effects (p-CNEs), a novel metric we introduce, crucial for adoption and market dynamics. We incorporate these features into a model of two-sided FinTech platform with potential failures and endogenous participation and fee structures. Our model predicts lenders’ single-homing, occasional lower fees for borrowers, asymmetric p-CNEs, and the predictive power of lenders’ p-CNEs in forecasting platform failures. Empirical evidence from China’s marketplace lending industry, characterized by frequent market entries, exits, and strong network externalities, corroborates our theoretical predictions. We find that lenders’ p-CNEs are systematically lower on declining or well-established platforms compared to those on emerging or rapidly growing platforms. Furthermore, lenders’ p-CNEs serve as an early indicator of platform survival likelihood, even at the initial stages of market development. Our findings provide novel economic insights into the functioning of multi-sided FinTech platforms, offering valuable implications for both industry practitioners and financial regulators.

We provide a novel explanation for the geographic dispersion of borrower-lender relationships based on information frictions rather than competition. Firms may strategically select distant banks to increase lenders’ information production costs, securing larger loans under information-insensitive contracts. Our model predicts that higher-quality firms prefer distant lenders for information-insensitive contracts, while lower-quality firms use local lenders with information-sensitive terms. Using transaction-level data from a major Chinese bank, we find strong empirical support: higher-rated firms exhibit greater propensity for distant borrowing; local loans show stronger negative correlation between amounts and interest rates; and distant loan pricing demonstrates weaker sensitivity to defaults.

Recently, the Chinese Government has strengthened the enforcement of bankruptcy laws to protect creditors’ rights. This study shed light on the effect of creditor protection on asset-debt maturity mismatch by employing a quasi-natural experiment in China. The results show that creditor protection mitigates maturity mismatch, and the effect is more pronounced among financially constrained firms. Results remain robust after the dynamic effects test, placebo test, propensity score matching approach, entropy balancing method, and controlling for COVID-19 shocks. Mechanism tests show that creditor protection decreases the cost of debt and reduces over-investment. The effect of creditor protection is pronounced in private companies, financially independent companies, and companies with secured loans. Creditor rights can alleviate maturity mismatch in firms with medium ownership concentration and managerial ownership levels. Economic consequences studies suggest that creditor protection reduces corporate default risk. This study reveals the mechanism and effect of creditor protection on asset-debt maturity mismatch in emerging markets, providing recommendations to policymakers for assessing and improving bankruptcy law regimes.

“How does a firm’s bankruptcy affect its regional environment?” is an open empirical question that has received little attention in the literature. We hypothesize that because enterprises provide funds to protect their regional environment, their default risk negatively impacts that environment. We analyze the impact of corporate default risk on environmental deterioration in the international setting to answer this question. Using a firm-level corporate default risk quarterly data from 2013q1 to 2020q4, we find that corporate default risk is positively associated with CO2 emissions and decomposed components. These findings are reliable in low-income and highly uncertain countries but weak in countries having more market competition. We also find that the negative impact of corporate default risk on the environment is more robust in countries with more population density and fewer forest area thresholds. Finally, using the instrumental variable approach, we provide preliminary evidence that firm-level political risk (for US and Canadian firms only) increases corporate default risk, leading to a degrading environment. Our findings are robust to alternative measurements of a firm’s default risk and environmental deterioration. Our research will help environmental authorities to consider corporate default risk as a determinant when formulating environmental-related strategies.

This study examines how firm-level exposure to the COVID-19 pandemic affects the speed of leverage adjustment among 3260 US-listed firms from 2019q1 to 2022q1. Using a novel measure of COVID-19 exposure, we find that higher exposure significantly reduces the speed at which firms adjust their leverage towards target levels. This effect is more pronounced for financially constrained firms and those operating in competitive markets. We further show that COVID-19 exposure adversely impacts corporate liquidity, default risk, and financial flexibility. Our findings highlight the role of exogenous shocks in shaping corporate financing decisions.

Using a large sample of US individuals, we show that individuals with higher levels of trust have lower likelihoods of default in household debt and higher net worth. The effect is driven by trust values inherited from cultural and family backgrounds more than by trust beliefs about others. We demonstrate a causal impact of trust on financial outcomes by extracting the component of trust correlated with early-life ex- periences. The effect of trust is more pronounced among females, those with lower education, lower income, lower financial literacy, and higher debt-to-income ratio. Further evidence suggests that enhancing individuals’ trust, to the right amount, can improve household financial well-being.

Since the global financial crisis of 2007–2009, preventing financial crises has become one of the most important objectives of regulators and banks. Although previous studies have identified the phenomenon of risk contagion in the banking system, the underlying mechanisms of risk contagion are still unclear. This study delves into the multi-stage contagion mechanism of liquidity risk based on interbank lending linkages and clearing rules and introduces a new index to quantify bank liquidity risk. We find that the contagion of liquidity risk is primarily determined by the network structure of risk exposures between banks in default and is not significantly influenced by the lending relationships of banks that remain solvent. The empirical results suggest that banks with high risk should be prioritized for cash injections to improve system liquidity. These findings offer new insights into financial risk contagion and practical recommendations for regulatory authorities formulating intervention strategies and for banks conducting risk management.

The level of corporate risk-taking impacts debt default as a crucial investment decision. Hence, this must be examined considering resource allocation. This study uses A-share listed companies from 2007 to 2021 as samples to empirically explore the impact and mechanism of corporate risk-taking level on debt default risk. The results show that corporate risk-taking can significantly inhibit debt default and the risk of debt default by promoting total factor productivity. Further, the higher the level of enterprise financialization of the firm, the higher the stock liquidity, and the higher the level of managerial confidence, the stronger the inhibitory effect of corporate risktaking on debt default. The heterogeneity analysis reveals that the inhibitory effect of corporate risk-taking on debt default is more significant in large-scale enterprises, enterprises with lower regulatory shareholdings, and enterprises with standard unqualified audit opinions. The study provides guidance for enterprises to improve the level of risk-taking and resource allocation efficiency effectively. Moreover, it provides empirical support for regulators to effectively prevent "waves of defaults" and even "waves of bankruptcies" in the real economy.

How does information sharing affect consumers' usage of FinTech credit? Using a unique dataset of ``Buy Now, Pay Later (BNPL)" users from a large digital platform and exploiting a credit reporting policy change, we document that consumers significantly reduce their usage of BNPL credit when the BNPL lender becomes subject to credit reporting regulation. This reduction is more pronounced among borrowers with previous default records, who also become more disciplined in repayment behaviors, than those without such records. The decrease in BNPL usage also leads to a reduction in online consumption, supporting the financial constraint hypothesis. Our results suggest that information sharing can help alleviate overborrowing and overspending, with stronger effects observed among younger borrowers, those who previously consumed more, or those with credit cards. We also highlight the synergies between BNPL lending and Big Tech platforms' ecosystems, which imperfectly substitute for formal enforcement institutions.