As digitizing personal financial data has facilitated easier data sharing, various U.S. states have identified the need for data privacy legislation and progressively enacted data privacy protection laws to ensure data security and usage transparency. Using a Difference-in-Difference-in-Differences model, we show that U.S. privacy legislation reduced racial disparities in mortgage lending as rejection rates for minority borrowers decreased 18% more compared to non-minority borrowers, and interest rates decreased by 2% more after privacy legislation has been passed. The effects are driven by increasingly data-driven mortgage decisions, reducing the reliance on soft information and the possibility of discriminatory practices, as well as by wider mortgage offers with lower interest rates in minority-dense and underserved banking areas, thereby reducing racial disparities and broadening credit access for minorities.

Compared to previous literature on external FinTech, this paper is more interested in the role played by bank FinTech. Based on panel data from Chinese commercial banks spanning 2010 to 2021, this paper investigates the impact of digital transformation on bank soundness and its potential mechanisms. The empirical findings demonstrate a positive association between digital transformation and bank soundness, driven primarily by strategic and management digitization. Mechanistic analysis indicates that digital transformation improves bank soundness by mitigating risk-taking behavior and promoting diversification. The positive effect of digital transformation is more pronounced in state-owned and joint-stock banks, banks with higher liquidity mismatch as well as in sub-samples with greater levels in external FinTech development and economic policies uncertainty. Additional analysis suggests that digital transformation can still enhance bank soundness even in the presence of relatively easy monetary and macroprudential policies, highlighting the harmonization and complementarity between internal innovation from digital transformation and external regulatory policies in maintaining banking stability. Overall, this paper contributes to the literature on bank FinTech, factors influencing bank stability. And it also provides a novel explanation for the relationship between financial innovation and financial stability.

The enforcement of peer-to-peer (P2P) registration requirements in mid-2018 triggered a P2P market meltdown, highlighting the inherent challenge faced by Chinese market participants in distinguishing between genuine and fraudulent fintech firms. The difference-in-difference results suggest that the too-big-to-fail (TBTF) perception can effectively halve investor outflows and borrower outflows during periods of uncertainty. Dynamic analysis further validates the parallel-trend assumption and underscores the persistent influence of TBTF perception. Moreover, the empirical findings suggest that, in the face of a market downturn, fintech market participants become unresponsive to all other certification mechanisms, including venture capital participation, custodian banks, and third-party guarantees.

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.

In this paper, we investigate whether those enterprises adopting digital finance exhibit higher values. On the basis of the constructed fintech-related lexicon developed by the machine learning-based Word2Vec model, we employ the frequency of fintech-related words (phrases) in the management discussion sections of annual reports as a proxy variable for the degree to which enterprises apply digital finance. We utilize panel data regression and mediation models based on data of Chinese A-share listed companies from 2016 to 2022 and explore the impact of this degree of digital finance application on enterprise value. We find that the degree to which enterprises apply digital finance elevates their values. The in-depth integration of digital technology and finance directly enhances enterprise value by reducing financing costs. Additionally, the effects are more evident among small-scale firms and enterprises located in regions with lower marketization levels. However, in the face of the impact of the COVID-19 pandemic, the positive effects on enterprises are relatively low.

This paper investigates the impact of local fintech development on analysts’ earnings forecast accuracy. We use the method of web text mining to construct the local fintech development index for empirical test and find that local fintech development significantly improves analysts’ earnings forecast accuracy by promoting firm digital transformation, improving firm information transparency, and alleviating the information asymmetry between firms and outsiders. Furthermore, this effect is more significant for analysts without equity pledge associations and those with weaker buy-side pressure. This study shows that local fintech development can optimize the capital market information environment.

A lack of sufficient information about potential borrowers is a major obstacle to access to financing from the traditional financial sector. In response to the need for better information to prevent fraud, to increase access to finance and to support balanced sustainable development, countries around the world have moved over the past several decades to develop credit information reporting requirements and systems to improve the coverage and quality of credit information. Until recently, such requirements mainly covered only banks. However, with the process of digital transformation in China and around the world, a range of new credit providers have emerged, in the context of financial technology (FinTech, TechFin and BigTech). Application of advanced data and analytics technologies provides major opportunities for both market participants – both traditional and otherwise – as well as for credit information agencies: by utilizing advanced technologies, participants and credit reporting agencies can collect massive amounts of information from various online and other activities (‘Big Data’), which contributes to the analysis of borrowing behavior and improves the accuracy of creditworthiness assessments, thereby enhancing availability of finance and supporting growth and development while also moderating prudential, behavioral and conduct related concerns at the heart of financial regulation. Reflecting international experience, China has over the past three decades developed a regulatory regime for credit information reporting and business. However, even in the context of traditional banking and credit, it has not come without problems. With the rapid growth and development of FinTech, TechFin and BigTech lenders, however, have come both real opportunities to leverage credit information and data but also real challenges around its regulation. For example, due to fragmented sources of borrower information and the involvement of many players of different types, there are difficulties in clarifying the business scope of credit reporting and also serious problems in relation to customer protection. Moreover, inadequate incentives for credit information and data sharing pose a challenge for regulators to promote competition and innovation in the credit market. Drawing upon the experiences of other jurisdictions, including the United States, United Kingdom, European Union, Singapore and Hong Kong, this paper argues that China should establish a sophisticated licensing regime and setout differentiated requirements for credit reporting agencies in line with the scope and nature of their business, thus addressing potential for regulatory arbitrage. Further, there is a need to formulate specific rules governing the provision of customer information to credit reporting agencies and the resolution of disputes arising from the accuracy and completeness of credit data. An effective information and data sharing scheme should be in place to help lenders make appropriate credit decisions and facilitate access to finance where necessary. The lessons from China’s experience in turn hold key lessons for other jurisdictions as they move from credit information to credit data regulation in their own financial systems.

Using four datasets of individuals’ digital payment and mutual fund investment records from a dominating fintech platform, we observe a robust U-shaped relation between individuals’ consumption and their financial wealth shocks. Contrary to the prediction of the wealth effect, individuals increase their consumption shortly after experiencing large positive and negative wealth shocks. The unexpected increase in consumption following negative wealth shocks is particularly pronounced among consumption categories with a “hedonic” nature, such as entertainment-related items. We show that this effect, termed “financial retail therapy,” is consistent with a dynamic model of Prospect Theory and evidence from a controlled laboratory experiment.

In recent years, numerous Chinese peer-to-peer (P2P) lending platforms have collapsed, prompting us to investigate the regulation and monitoring of the fintech industry. Using a unique dataset of P2P lending platforms in China, we examine the effect of government monitoring on platform collapses. Exploiting platforms’ locational proximity to regulatory offices as a proxy for government monitoring, we show that greater geographical distance results in a higher likelihood of platform collapse. Specifically, for every 10% increase in the driving distance from the platform to the local regulatory office, the likelihood of collapse increases by 10.2%. To establish causality, we conduct a differencein-differencesanalysis that exploits two exogenous shocks: government office relocation and subway station openings. We further explore two underlying channels: the information channel through which greater regulatory distance reduces the likelihood of regulators’ onsite visits and the resource constraint channel, through which greater regulatory distance significantly increases the local regulatory office’s monitoring costs. Overall, this study highlights the importance of onsite regulatory monitoring to ensure the viability of online lending platforms.

We study the income inequality among streamers using the administrative data of a leading Chinese live-streaming platform. The live-streaming technology enables a superstar to produce new entertainment products matched with demand and occupies a larger market share. Imagine an extreme case; the best streamer hosts live for 24 hours, earns all possible income, and leaves zero time for other streamers. Our data show that the income distribution of the highest-paid streamers follows Zipf’s Law and appears to be even more concentrated than any offline business: NBA top players, Forbes celebrities, and billionaires. Income inequality increased rapidly as the platform expanded from 2018 to 2020 — for example, the income share of the platform’s top 10 streamers increased from 14.82% to 45.15% as its revenue grew by 142%. To estimate inequality elasticity to the market size, we study four quasi-experimental shocks: potential market size proxied by economic development and Fintech coverage, quarter-end revenue spikes induced by the seasonal incentive regime, user surge induced by capital raising, and the Covid-19 lockdown in Wuhan. Gini coefficient elasticity ranges fromm1.3% to 10.6% estimated from the cross-city variations (local economic development and Covid-19 Wuhan lockdown); the time-series variations (quarter-end and user surge before capital raising) imply an elasticity ranging from 3.6% to 25.5%.