Fintech Lenders Research Articles

The Impact of FinTech on Discrimination in Mortgage Lending

The mortgage market has historically been plagued by racial discrimination, and recent data show ongoing disparities in the terms offered to minority borrowers by traditional lenders. The mortgage market has changed rapidly in the past decade, however, due to the rise of technology-based (FinTech) lenders. In this paper, I show that FinTech lenders show little to no gap in the terms provided to Black and Hispanic borrowers after adjusting for GSE credit-risk pricing determinants and loan size. This fact is shown using proprietary data on borrowers to better study racial bias and FinTech loans. The paper further shows that, in a matched analysis of observationally similar Black and Hispanic and non-Black or Hispanic borrowers, FinTech lenders provide statistically indistinguishable terms to the two groups. This stands in sharp contrast to the large discrepancies found under identical tests for traditional lenders. This matched analysis, wherein borrowers with similar characteristics such as income and age are compared, is supported by an analysis of preference and shopping heterogeneity in the National Survey of Mortgage Originations. Finally, I show that FinTech penetration in a region had consequences on legacy lender terms as well. Greater FinTech penetration in a zip code is associated with smaller discrepancies even among legacy lenders.

Trust As an Entry Barrier: Evidence from FinTech Adoption

This paper studies the role of trust in incumbent lenders (banks) as an entry barrier to emerging FinTech lenders in the credit markets. The empirical setting exploits the outburst of the Wells Fargo scandal as a negative shock to the trust in banks. Using a difference-in-differences framework, I find that increased exposure to the Wells Fargo scandal leads to an increase in the probability of borrowers using FinTech as mortgage originators. Utilizing political affiliation to proxy for the magnitude of trust erosion in banks in a triple-differences specification, I find that, conditional on the same exposure to the scandal, a county experiencing more trust erosion has a larger increase in FinTech share relative to a county experiencing less trust erosion. Treatment effect heterogeneity estimation from generic machine learning inference suggests that trust is less critical in FinTech adoption for the African American borrowers.

Marketplace Lending: Matching Small Businesses with Specialized Fintech Lenders

Fintech promises improvements in access to credit for small businesses through more efficient search and better pricing. Using novel data from a marketplace platform of 115,000 loan offers from 46 online lenders I show that the primary contribution of fintech is not in precisely measuring and pricing risk, but rather in facilitating search between small firms and preferred-habitat lenders. Loan rate offers are largely unexplained by firm characteristics and differ substantially even for the same applicant. The dispersion in offers is largely explained by the fact that lenders have preferred habitats -- lending to borrowers of certain risk types and charging relatively uniform rates to all applicants. Interest rates sort borrowers such that the highest interest rate lenders match with firms that have been rejected by lenders with lower rates. This sorting leads to equilibrium loan rates that appear to be closely tied to the characteristics of the firm. The findings highlight the importance of marketplace platforms that reduce search frictions for firms seeking credit among lenders with distinct lending practices.

FinTech Lending

In this paper, we review the growing literature on FinTech lending – the provision of credit facilitated by technology that improves the customer-lender interaction or lenders’ screening and monitoring of borrowers. FinTech lending has grown rapidly, though in developed economies like the U.S. it still only accounts for a small share of total credit. An increase in convenience and speed appears to have been more central to FinTech lending’s growth than improved screening or monitoring, though there is certainly potential for the latter, as is the case for increased financial inclusion. The COVID 19 pandemic has shown potential vulnerabilities of FinTech lenders, although in certain segments they have displayed rapid growth.

Fintech Lending and Mortgage Credit Access

Following the 2008 financial crisis, mortgage credit tightened and banks lost significant mortgage market share to nonbank lenders, including to fintech firms recently. Have fintech firms expanded credit access, or are their customers similar to those of traditional lenders? Unlike in small business and unsecured consumers lending, fintech mortgage lenders do not have the same incentives or flexibility to use alternative data for credit decisions because of stringent mortgage origination requirements. Fintech loans are broadly similar to those made by traditional lenders, despite innovations in the marketing and the application process. However, nonbanks market to consumers with weaker credit scores than do banks, and fintech lenders have greater market shares in areas with lower credit scores and higher mortgage denial rates.

Fintech Borrowers: Lax Screening or Cream-Skimming?

Abstract We study the personal credit market using unique individual-level data covering fintech and traditional lenders. We show that fintech lenders acquire market share by lending first to higher-risk borrowers and then to safer borrowers, and rely mainly on hard information to make credit decisions. Fintech borrowers are significantly more likely to default than neighbor individuals with the same characteristics borrowing from traditional financial institutions. Furthermore, they tend to experience a short-lived reduction in the cost of credit, because their indebtedness increases more than non-fintech borrowers after loan origination. However, fintech lenders’ pricing strategies are likely to take this into account.

Banking sector earnings management using loan loss provisions in the Fintech era

PurposeThis paper analyzes banking sector earnings management using loan loss provisions (LLPs) in the Fintech era.Design/methodology/approachRegression methodology was used to examine earnings management in the Fintech era.FindingsThe findings show evidence for bank income smoothing using LLPs. There is greater income smoothing in the second-wave Fintech era compared to the first-wave Fintech era, and the presence of strong institutions did not lower income smoothing in the second-wave era. Bank income smoothing is also greater in (1) Bank of International Settlement (BIS) and EU countries than in non-EU countries and G7 countries, (2) well-capitalized banking sectors and (3) during economic booms in the second-wave Fintech era.Practical implicationsThe competition for loans and deposits by banks and Fintech lenders in the second-wave Fintech era created additional incentives for banks to engage in income smoothing to report competitive and stable earnings.Originality/valueThe study uses a unique approach to detect country-level earnings management in the banking sector. Also, this study extends the bank earnings management literature by introducing the Fintech era as a determinant of the extent of bank earnings management.

What Do Two Million Credit Lines Say About the FinTech Fragility?

We analyze lending by traditional as well as FinTech lenders during COVID-19. Comparing samples of FinTech and bank loan records across the outbreak, we find that FinTech companies are more likely to expand credit access to new and financially constrained borrowers after the start of the pandemic. However, this increased credit provision may not be sustainable; the delinquency rate of FinTech loans triples after the outbreak, but there is no significant change in the delinquency of bank loans. Borrowers holding both loan types prioritize the payment of bank loans. These results shed light on the benefits provided by shadow banking in a crisis and hint at the potential fragility of such institutions when delinquency rates spike.

The Racial Landscape of Fintech Mortgage Lending

ABSTRACT Little is known about racial patterns in fintech mortgage lending, despite evidence of racial disparities in the broader mortgage market. This study leverages 2015–2017 Home Mortgage Disclosure Act data to assess disparities in lending outcomes between White and non-White applicants and between neighborhoods of varying racial composition in the United States’ 200 largest metropolitan areas at fintech and traditional lenders. Results of a series of binary logistic regression models suggest disparities in rates of loan approvals between White and similarly qualified non-White applicants are substantively small overall, but lower at fintech lenders relative to traditional lenders, most substantially for Latinos. Non-White applicants are more likely to receive subprime terms relative to similarly qualified White applicants at both lender types, and disparities in rates of subprime loan receipt between Black and similarly qualified White applicants are greater at fintech lenders than traditional lenders. Neighborhood racial composition has a mixed but substantively small impact on approval rates at both lender types. However, both lender types distribute subprime credit to non-White neighborhoods at significantly higher rates than to White neighborhoods. Findings suggest fintech lending contributes to racial and spatial disparities in subprime mortgage lending and warrants increased scrutiny from regulators.

The impacts of fintech on small business borrowing

Consolidation in banking raise concerns about exclusion and predatory practices in small-business lending. Fintech lenders, largely unregulated credit providers with lending decisions and loan terms determined primarily by algorithm, have rapidly increased their lending to small businesses. I analyze loan-level data on consumer and small business loans from Fintech lenders and a comparison sample of small-business loans from regulated bank lenders. Fintech small-business loans charge average annual interest rates 3 percentage points higher than consumer loans from the same lender and 4 to 7 percentage points higher than small business loans from regulated banking entities. The large interest-rate premium points to the need for regulatory clarity and additional supervision to protect this crucial market segment from predatory non-bank lenders.

Fintech Lenders’ Responses to Senate Probe Heighten Fears of Educational Redlining

The SBPC found that companies' use of information on credit applicants' education history resulted in ‘educational redlining,’ penalizing borrowers who attended minority-serving institutions. In response to our findings, five United States Senators wrote to a range of companies involved in the collection and use of educational data to demand detailed explanations of this data’s utilization in credit underwriting. These companies' responses only heightened concerns raised in our initial research and revealed additional gaps in fair lending oversight.

The Rise of Finance Companies and FinTech Lenders in Small Business Lending

We document that finance companies and FinTech lenders increased lending to small businesses after the 2008 financial crisis. We show that most of the increase substituted for a reduction in lending by banks. In counties where banks had a larger market share before the crisis, finance companies and FinTech lenders increased their lending more. By 2016, the increase in finance company and FinTech lending almost perfectly offset the decrease in bank lending. We control for firms' credit demand by examining lending by different lenders to the same firm, by comparing firms within the same narrow industry, and by comparing firms pledging the same type of collateral. Consistent with the substitution of bank lending with finance company and FinTech lending, we find that reduced bank lending had no effect on employment, wages, new business creation, or business expansion. Our results show that finance companies and FinTech lenders are major suppliers of credit to small businesses and played an important role in the recovery from the 2008 financial crisis.

The Impacts of Fintech on Small Business Borrowing

Fintech lending to small businesses is growing rapidly in the United States, but the industry remains largely unregulated. In this study, I examine borrower outcomes when a borrower states that they are using credit from a fintech for small business purposes; I subsequently examine the differences in loan terms for regulated versus unregulated small business credit. I find that small business loans are charged a higher rate of interest than consumer loans from the same fintech lender, and also find that small business fintech loans have worse terms than small business loans from regulated banking entities. I propose a regulatory framework to offer small business borrowers protection from predatory fintech lenders and to clarify the proper regulators of this growing industry.

Do Fintech Lenders Fill the Credit Gap? Evidence from Local Mortgage Demand Shocks

After exogenous demand shocks caused by natural disasters, FinTech lenders are more responsive to increased demand for reconstruction mortgages than traditional banks and non-FinTech shadow banks. Both FinTech and traditional banks increase credit supply, but FinTech supply is more elastic without increases in risk-adjusted interest rates or delinquency rates. Comparing lending supply channels, banks respond to regulatory incentives to lend to damaged areas, whereas FinTech lenders supply more credit when traditional banks rely more on balance sheet financing and physical branch networks. Compared to traditional banks, FinTech lenders increase supply elasticity more aggressively in response to local competitive pressure.

Fintech Borrowers: Lax-Screening or Cream-Skimming?

We study the personal credit market using unique individual-level data covering fintech and traditional lenders. We show that fintech lenders acquire market share by first lending to higher-risk borrowers and then to safer borrowers, and mainly rely on hard information to make credit decisions. Fintech borrowers are significantly more likely to default than neighbor individuals with the same characteristics borrowing from traditional financial institutions. Furthermore, they tend to experience only a short- lived reduction in the cost of credit, because their indebtedness increases more than non-fintech borrowers a few months after loan origination. However, fintech lenders' pricing strategies are likely to take this into account. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Open Banking: Credit Market Competition When Borrowers Own the Data

Open banking facilitates data sharing consented by customers who generate the data, with a regulatory goal of promoting competition between traditional banks and challenger fintech entrants. We study lending market competition when sharing banks' customer data enables better borrower screening or targeting by fintech lenders. Open banking could make the entire financial industry better off yet leave all borrowers worse off, even if borrowers could choose whether to share their data. We highlight the importance of equilibrium credit quality inference from borrowers' endogenous sign-up decisions. When data sharing triggers privacy concerns by facilitating exploitative targeted loans, the equilibrium sign-up population can grow with the degree of privacy concerns.

Open Banking: Credit Market Competition When Borrowers Own the Data

Open banking facilitates data sharing consented by customers who generate the data, with a regulatory goal of promoting competition between traditional banks and challenger fintech entrants. We study lending market competition when sharing banks’ customer data enables better borrower screening or targeting by fintech lenders. Open banking could make the entire financial industry better off yet leave all borrowers worse off, even if borrowers could choose whether to share their data. We highlight the importance of equilibrium credit quality inference from borrowers’ endogenous sign-up decisions. When data sharing triggers privacy concerns by facilitating exploitative targeted loans, the equilibrium sign-up population can grow with the degree of privacy concerns.

The Disruption of the Prudential Regulatory Framework

ABSTRACT This article first explores how the prudential regulatory framework may be drastically changed as a result of the adoption of regtech and suptech innovations. The adoption of a shared data architecture, whereby the regulator is provided with seamless access to the accounts of regulated firms, may result in the development of a centralized approach to regulatory modelling, thus bypassing the existing partition between internal models and standardized approaches. Secondly, this article discusses how, thanks to the adoption of advanced predictive analytics, non-bank fintech lenders can gain insights into borrowers’ creditworthiness irrespective of banks’ control over traditional sources of information for credit scoring. This, though, may give rise to a new type of strategic gaming, this time by borrowers keen to maximize their chances of being granted a loan. In any case, preventing banks from adopting these new methods for the purpose of regulatory modelling might weaken their competitiveness. Thirdly, this article explores whether the transition to a centralized approach for credit risk management would conflate the distinction between microprudential and macroprudential interventions, in particular with respect to the countercyclical macroprudential policy. Finally, the article argues that non-bank (fintech) firms could be persuaded to coalesce behind endorsement of the new regulatory shared data platform in return for the support from the central bank. In this way, the regulatory perimeter could be extended in order to steer the adoption of financial innovations in a manner that benefits society overall.

Preventing Predation & Encouraging Innovation in Fintech Lending

More than 20 years ago, IBM's Deep Blue vanquished chess grandmaster and reigning world chess champion, Garry Kasparov, in a pair of best-of-six matches. Since then, numerous companies have invested large sums of money to develop additional game-playing, machine-learning algorithms. They have enjoyed significant successes. For example, in 2016, AlphaGo demolished Lee Sedol, a world champion Go player, 4-1 in a series of matches by, among other things, making “a move no human would ever play, stunning experts and fans and utterly wrong-footing world champion Lee Sedol.” In addition, Libratus defeated “four of the top-ranked human Texas Hold'em players in the world over the course of 120,000 hands” during a twenty-day poker competition in 2017. Other algorithms have also defeated top players in checkers, chess, Jeopardy!, and Scrabble. These companies are not trying to dominate your family game nights. Instead, they are betting that many of the techniques and strategies used to create world-class game-playing algorithms can be deployed for other purposes as well. In other words, they are looking to create general-purpose artificial intelligence (AI) systems. One use for AI systems is to improve credit-underwriting models. The regulation of these types of AI systems is the focus of this paper. This new breed of lenders, often called fintech lenders, are amassing hordes of data (Big Data) and using machine- learning techniques honed in the game-playing context to improve existing credit-underwriting models. Fintech lenders commonly use nontraditional, tech-centric methods to market themselves to prospective borrowers, evaluate borrower creditworthiness, and to match prospective borrowers with sources of credit. Examples include Lenddo and ZestFinance. Fintech lenders often claim that they can lower loan origination costs and loan default rates, thus improving credit accessibility. This essay proceeds as follows. First, it briefly explains fintech lending, focusing on its differences from traditional lending. Next, it highlights the promise and peril of fintech lending. Finally, it considers how state and federal regulators might deploy their powers to prohibit unfair acts and practices to encourage the promise and avoid the peril of fintech lending. A brief conclusion follows.

The roles of alternative data and machine learning in fintech lending: Evidence from the LendingClub consumer platform

AbstractThere have been concerns about the use of alternative data sources by fintech lenders. We compare loans made by LendingClub and similar loans that were originated by banks. The correlations between the rating grades (assigned by LendingClub) and the borrowers’ FICO scores declined from about 80% (for loans originated in 2007) to about 35% for recent vintages (originated in 2014–2015), indicating that nontraditional data (not already accounted for in the FICO scores) have been increasingly used by fintech lenders. The rating grades perform well in predicting loan default. The use of alternative data has allowed some borrowers who would have been classified as subprime by traditional criteria to be slotted into “better” loan grades, allowing them to obtain lower priced credit.