We study the effectiveness of saving goals in increasing individuals' savings using data from a Fintech app. Using a difference‐in‐differences identification strategy that randomly assigns users into a group of beta testers who can set goals and a group of users who cannot, we find that setting goals increases individuals' savings rate. The increased savings within the app do not reduce savings outside the app. Moreover, goal setting helps those individuals previously identified as having the lowest propensity to save. Matching App user survey responses to their behavior highlights the relative merits of monitoring and concreteness channels in explaining our findings

Central banks around the world are exploring the possibility of Central Bank Digital Currencies (CBDCs) for retail and wholesale use. While no major economy is yet to fully introduced a CBDC, some countries have begun pilot programs. The purpose of this paper is to highlight the potential benefits and risks associated with CBDCs, including challenges and opportunities associated with proposed CBDC regulation in the United States and the European Union. The paper also discusses the CBDC landscape in Asia. It highlights some of the key findings of the research presented in this special issue on FinTech and CBDCs. Lastly, the paper offers thoughts for potential future research in areas such as the actual designs of CBDCs and their uses, ‘DeFi’ versus ‘CeFi’, their interoperability and stability, and concerns over cybercrime

Technology-based (“FinTech”) lenders increased their market share of U.S. mortgage lending from 2% to 8% from 2010 to 2016. Using loan-level data on mortgage applications and originations, we show that FinTech lenders process mortgage applications 20% faster than other lenders, controlling for observable characteristics. Faster processing does not come at the cost of higher defaults. FinTech lenders adjust supply more elastically than do other lenders in response to exogenous mortgage demand shocks. In areas with more FinTech lending, borrowers refinance more, especially when it is in their interest. We find no evidence that FinTech lenders target borrowers with low access to finance.Received June 1, 2017; editorial decision November 5, 2018 by Editor Wei Jiang

Does the recent proliferation of technology in lending process have an impact on business loan market competition? Using a theoretical model that assumes heterogeneity in lenders’ screening abilities and borrowers’ investment horizons, we show that FinTech (Traditional) lenders primarily supply unsecured (asset-backed) loans to borrowers with short-term (long-term) projects. The model builds on the interplay between screening ability and collateral requirements to characterize the competition between two ex-ante symmetric lenders. Lenders use screening technology and collateral requirements to mitigate competition and restrict the supply of credit through an endogenous segmentation of the loan market. As information technology improves, the effect on credit supply and equilibrium interest rates becomes more nuanced and depends on the market segment. The results offer a supply-side explanation for the growth of unsecured lending

This paper compares the predictive power of credit scoring models based on machine learning techniques with that of traditional loss and default models. Using proprietary transaction-level data from a leading fintech company in China, we test the performance of different models to predict losses and defaults both in normal times and when the economy is subject to a shock. In particular, we analyse the case of an (exogenous) change in regulation policy on shadow banking in China that caused credit conditions to deteriorate. We find that the model based on machine learning and non-traditional data is better able to predict losses and defaults than traditional models in the presence of a negative shock to the aggregate credit supply. This result reflects a higher capacity of non-traditional data to capture relevant borrower characteristics and of machine learning techniques to better mine the non-linear relationship between variables in a period of stress

We examine how financial technology affects households in terms of personal bankruptcy by leveraging exogenous variation in marketplace credit supply to Connecticut and New York residents. We document a persistent rise in bankruptcies in the affected states following sharp decreases in marketplace lending, particularly among low-income households and in areas where marketplace loans for financing medical bills are severely rationed. Borrowers’ indebtedness or local economic conditions do not explain the results. The supply of other consumer credit by banks and finance companies remains unaffected, suggesting that the observed increase in bankruptcies arises principally from reversing access to marketplace credit

We examine how increased competition stemming from an innovation in financial technology influences sell-side analyst research quality. We find that firms added to Estimize, an open platform that crowdsources short-term earnings forecasts, experience a pervasive and substantial reduction in consensus bias and a limited increase in consensus accuracy relative to matched control firms. Long-term forecasts and investment recommendations remain similarly biased, alleviating the concern that the documented reduction in bias is a response to broad economic forces. At the individual analyst level, we find that bias reduction is more pronounced among close-to-management analysts, and that more biased analysts respond by reducing their coverage of Estimize firms. The collective evidence suggests that competition from Estimize improves sell-side research quality by discouraging strategic bias

Open banking facilitates data sharing consented to by customers who generate the data, with the regulatory goal of promoting competition between traditional banks and challenger fintech entrants. We study lending market competition when sharing banks’ customer transaction data enables better borrower screening for fintechs. Open banking promotes competition if it helps level the playing field for all lenders in screening borrowers; however, if it over-empowers fintechs, it can also hinder competition and leave all borrowers worse off. Due to the credit quality inference from borrowers’ sign-up decisions, this remains true even if borrowers have the control of whether to share their banking data. We also study extensions with fintech affinities and data sharing on borrower preferences

Compared with the previous literature on external FinTech, this paper is more interested in the role played by bank FinTech. On the basis of panel data from Chinese commercial banks spanning 2010–2021, 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 and in the subsamples with greater levels of external FinTech development and economic policy uncertainty. Additional analysis suggests that digital transformation can still enhance bank soundness even in the presence of relatively lenient 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, which focuses on the factors influencing bank stability. This study also provides a novel explanation for the relationship between financial innovation and financial stability

Do coordination failures constrain financial technology adoption? Exploiting the Mexican government’s rollout of 1 million debit cards to poor households from 2009 to 2012, I examine responses on both sides of the market and find important spillovers and distributional impacts. On the supply side, small retail firms adopted point-of-sale terminals to accept card payments. On the demand side, this led to a 21 percent increase in other consumers’ card adoption. The supply-side technology adoption response had positive effects on both richer consumers and small retail firms: richer consumers shifted 13 percent of their supermarket consumption to small retailers, whose sales and profits increased