Using unique data on Alipay users' investment accounts, we find that, in addition to generating better performance than investors’ self-directed portfolios, robo-advising has a positive spillover effect on its adopters in terms that it improves their investment behaviors. Investors have more diversified portfolios and exhibit fewer behavioral biases in portfolio management and fund choices in their self-directed accounts after adopting robo-advising. The spillover effect is more prominent for adopters who interact with the service more actively and who were less sophisticated before adopting the app. We also find that adopters learn from the robo-advisor by simply imitating its portfolios or strategies. Collectively, this study provides large-sample, non-laboratory evidence that robo-advising effectively plays a role in educating investors through repeated interactions with its adopters and setting investment models that are easy to follow.

This study investigates the influence of mobile payment application use on crime rates. Using a unique database of verdicts from criminal courts in China and an index measuring the extent of mobile payment usage, we find that a one standard deviation increase in mobile payment adoption and usage leads to an 11% decrease in the theft rate. Furthermore, the effect of widespread mobile payment adoption on theft rates is more pronounced in areas characterized by a higher prevalence of cash transactions. These findings suggest that the decrease in cash circulation in society due to mobile payment use can reduce incentives for theft. However, we do not find evidence linking mobile payment usage to other types of criminal activity, including robbery, arson, brawling, homicide, and serious injury by vehicle.

The past decade has witnessed a phenomenal rise of digital wallets, and the COVID-19 pandemic further accelerated their adoption globally. Such e-wallets provide not only a conduit to external bank accounts but also internal payment options, including the ever-popular Buy-Now-Pay-Later (BNPL). We examine, for the first time, e-wallet transactions matched with merchant and consumer information from a world-leading provider based in China, with around one billion users globally and a business model that other e-wallet providers quickly converge to. We document that internal payment options, especially BNPL, dominate both online and on-site transactions. BNPL has greatly expanded credit access at the extensive margin through its adoption in two-sided payment markets. While BNPL crowds out other e-wallet payment options, it expands FinTech credit to underserved consumers. Exploiting a randomized experiment, we also find that e-wallet credit through BNPL substantially boosts consumer spending. Nevertheless, users, especially those relying on e-wallets as their sole credit source, carefully moderate borrowing when incurring interest charges. The insights likely prove informative for economies transitioning from cash-heavy to cashless societies where digital payments and FinTech credit see the largest growth and market potential.

We explore the relationship between fintech, macroprudential policies, and commercial bank risk-taking. Based on system generalized method of moment modeling on a panel data of 114 commercial banks in China from 2013 to 2020, results show that there are functional differences in the impact of fintech on bank risk-taking. Payment and settlement technology (PST), capital raising technology (CRT) and investment management technology (IMT) are positively correlated with bank risk-taking. In contrast, market facility technology (MFT) negatively correlates with bank risk-taking. We also find that macroprudential policies weaken the promotion effect of CRT on bank risk-taking and strengthen the inhibition effect of MFT on bank risk-taking while having no significant moderating effect on PST and bank risk, IMT and bank risk. Further, the micro characteristics of banks (capital adequacy ratio, asset scale, liquidity level) affects the moderating strength of macroprudential policies. Various robustness tests confirm our conclusions.

Fintech firms mobilize information technology to provide intermediation services using a broker methodology, whereas dealer banks intermediate using leveraged balance sheets. The integration of Fintech into banking may reduce the unit cost of intermediation by shifting the production function from dealer to broker. A “Fintech score” is derived using nonlinear and machine learning algorithms that show on-balance sheet lending for low Fintech score dealer banks versus securitization, brokered deposits, and non-interest income for high score, broker banks. Using Data Envelopment and Stochastic Cost Frontier Analyses, we find that banks with higher Fintech scores are more operationally efficient and resilient in crises.

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 investigate the effect of the information environment on regulatory monitoring and platform collapse. Using the platforms’ proximity to regulatory offices as a proxy for information asymmetry, we show that an increase in distance reduces regulatory monitoring and increases the likelihood of platform collapse. Specifically, for every 1% increase in the driving distance between the local regulatory office and a P2P lending platform’s office, the platform’s likelihood of collapse increases by 1.011%. To establish causality, we conduct a difference-in-differences analysis that exploits two exogenous shocks: government office relocation and subway station openings. We provide evidence that proximity enhances monitoring quality by facilitating soft information collection, reducing platform failures. We further find two channels of this effect: (1) the information channel through which greater regulatory distance reduces the likelihood and frequency of regulators’ on-site visits and (2) 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 the acquisition of soft information for regulatory monitoring to ensure the viability of fintech firms.

This study investigates the effect of financial technology (FinTech) development on stock price crash risk. We show that the development of FinTech can inhibit management from deliberately hiding bad news and alleviate information asymmetry, thereby reducing stock price crash risk. This effect is more pronounced among non-state-owned enterprises, firms with poor information environments and low-quality internal controls, and those in competitive industries and regions with high marketization. Overall, these findings suggest that the development of FinTech can mitigate the deliberate concealment of bad news by management and improve the timeliness of disclosure, leading to lower risks faced by investors.

We examine the role of borrowers' digital footprints in debt collection. Using a large sample of personal loans from a fintech lender in China, we find that the information acquired by the lender through borrowers' digital footprints can increase the repayment likelihood on delinquent loans by 18.5%. The effect can be explained by two channels: bonding borrowers' obligations with their social networks and locating borrowers' physical locations. Moreover, the lender is more likely to approve loan applications from borrowers with digital footprints, even though these borrowers may occasionally have a higher likelihood of delinquency. The use of digital footprints can remain legitimate under stringent privacy protection regulations and fair debt collection practices. Our findings suggest that digital footprints, as a new type of collateral, can ultimately enhance financial inclusion by facilitating the lender's collection of delinquent loans.

机器人投资顾问（RIA）能为投资者提供信息，帮助其决策，已出现在多种投资平台上。利用2020和2021年中国最大投资平台的账户数据，本文检验了机器人投资顾问提供的不同服务。总体上看，机器人投顾的使用率越高，未来的净收益和风险调整后收益也越高，资产更多元，风险也越高。和信息服务相比，建议服务能对未来交易行为产生更大影响；这可能是因为机器投顾的建议比较简单，容易理解，因此能更好地被遵循，这与信息服务提供的海量信息形成对比。本文没有发现现有机器人投顾能移除行为偏差的证据。

我们发现，股市泡沫和信贷市场中的信用风险存在因果关系。我们分析了来自人人贷（国内头部P2P众筹平台）的超过45万笔贷款数据，时间为2015年，当时A股正经历非理性的大起大落。随着上证综指突破3500点，散户们积极进入股市，并通过P2P平台融资，我们发现，此时P2P平台贷款的违约率以及违约程度都有了大幅提高。对于低质量贷款以及过分自信的贷款者，这种效应更加显著。其他一系列P2P市场状态指标，也都出现恶化。总之，我们认为，金融科技的发展会扩大金融风险，促使风险在不同市场之间蔓延。