Yingju Ma

Leveraging data from a major fintech lending provider for SMEs in China and a natural experiment from a temporary and geographically discontinuous interest rate discount policy, we analyze the presence of information asymmetries, selection, and market power. Our findings reveal a decrease in the average probability of loan repayment following interest rate reductions, indicating that selection is advantageous. We explain the effect in a generalized Stiglitz-Weiss model accounting for a broad distribution of borrower risk and returns, supported by empirical evidence. Building on our estimated demand elasticities and selection effects, we assess the welfare implications of information-sharing and pro-competition policies. Our counterfactual analysis reveals that ensuring competition and mandating information sharing across financial providers results in the largest surplus gains. Meanwhile, optimal information disclosure significantly mitigates monopoly distortions by increasing credit supply to riskier borrowers.

Fintech credit through digital payment platforms reaches 500 million Chinese consumers, many of whom hold no traditional bank credit line. We use de-identified administrative data from Alipay covering 60 months of full payment activity, both online and in-person, across payment instruments and consumption categories, for a sample drawn from a near-billion-consumer population. This provides the first integrated view of consumer payment and credit behavior in China. Using the January 2020 Covid-19 outbreak as a quasi-exogenous liquidity shock and a matched difference-in-differences design on 434,398 consumers, we find that fintech credit users sustain 10.2% higher consumption over the following two years. The response is concentrated among consumers facing binding liquidity constraints: low income, less developed regions, and limited external bank credit. The effect is further amplified among less financially sophisticated consumers, suggesting that fintech credit lowers the financial expertise required to access and use credit effectively.

Breakthroughs in artificial intelligence are reshaping credit markets by enabling real-time risk management, not merely better screening at origination. Using proprietary, high-frequency data on a sample of 800,000 small-business borrowers drawn from a large platform lender, covering January 2018 to March 2021, we show that the AI-powered lending system actively adjusts credit access, credit lines, and interest rates as risk conditions change, leveraging real-time big data on borrowers to contain losses in the unsecured credit loans. Employing an instrumental-variables design, we identify a causal effect of platform dependence on default: greater dependence lowers default, consistent with stronger repayment incentives from continued platform participation. These findings reveal a distinctive feature of AI-powered platform lending: it is most effective when algorithmic risk management is integrated with platform access and activity.

I study a production-grade generative-AI tool that offers licensed insurance brokers real-time reply suggestions during customer advisory conversations. The field experiment, conducted at China's largest insurance brokerage Ant Insurance, covers over 1,000 brokers and premium revenue equivalent to tens of millions of US dollars. Randomization is at the customer level: brokers see AI suggestions only when serving treated customers. AI assignment raises premium revenue by 30.7%, operating through both an extensive-margin gain in conversion (16% relative increase) and an intensive-margin gain in per-policy premium (12%, conditional on purchase). The setting enables a further decomposition into adoption frequency and per-adoption return: junior brokers adopt AI suggestions more frequently, whereas revenue gains are larger for senior brokers. AI exposure also induces broker learning, with gains transferring to non-AI interactions.