Fintech, regulatory arbitrage, and the rise of shadow banks

Buchak, Greg; Matvos, Gregor; Piskorski, Tomasz; Seru, Amit

doi:10.1016/j.jfineco.2018.03.011

Cited by 1,093 publications

(725 citation statements)

References 41 publications

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“…Jagtiani, Lambie-Hanson, and Lambie-Hanson (2019) find that, for FHA mortgage borrowers (i.e., borrowers who are more likely to be underserved than the conventional mortgage borrowers), fintech lenders offer a lower rate than traditional mortgage lenders on average. In contrast, Buchak et al (2017), focusing on conventional mortgage loans, find evidence that fintech mortgage borrowers are among the borrowers who value fast and convenient services and that fintech lenders command an interest rate premium for their services. Another interesting study that looked at risk pricing by Lending-Club found that the rates charged to higher-risk borrowers were not large enough to compensate for a higher probability of default (see Emekter, Tu, Jirasakuldech, & Lu, 2014).…”

Section: The Literaturementioning

confidence: 95%

“…For example, some fintech lenders can identify whether the loan applications are submitted from a high-crime area or in an area where factories are being shut down or relocated. Previous studies have found evidence that local economic information could serve as a possible relevant source of nontraditional information by fintech lenders (Alyakoob, Rahman, & Wei, 2017;Bertsch, Hull, & Zhang, 2016;Buchak, Matvos, Piskorski, & Seru, 2017;Chen, Hanson, & Stein, 2017;Crowe & Ramcharan, 2013;Havrylchyk, Mariotto, Rahim, & Verdier, 2018;Jagtiani & Lemieux, 2018).…”

Section: The Literaturementioning

confidence: 99%

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

Jagtiani

Lemieux

2019

Financial Management

220

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There 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.

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Section: The Literaturementioning

confidence: 95%

Section: The Literaturementioning

confidence: 99%

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

Jagtiani

Lemieux

2019

Financial Management

220

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“…Borst (2013) and Wang et al (2015) attributed the rapid growth of shadow banking in China to interest rate regulation, which restricts the ceilings of deposit rates offered by banks to savers. Buchak et al (2018) quantitatively identified that regulation and fintech technology accounted for approximately 90 percent of the growth in mortgage lending, one important part of shadow banking in the US.…”

Section: Literature Reviewmentioning

confidence: 99%

“…They showed that fintech lenders process mortgage applications faster and adjust supply more elastically than other lenders in response to mortgage demand shocks, which suggests that technological innovation may have improved the efficiency of financial intermediation in the mortgage market. Buchak et al (2018) found that fintech lenders offer a higher quality product and charge a premium of 14-16 basis points. The combination of higher interest rates and growing market shares suggests an increasing consumer demand for fintech services as the technology improves.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The quantitative model in their study suggests that regulation accounts for approximately 60 percent of shadow bank growth, while technology accounts for approximately 30 percent. While Buchak et al (2018) used fintech and regulation to account for the growth of shadow banks, we provide a framework to examine how fintech development and changes in regulations will influence the stability of shadow deposits.…”

Section: Introductionmentioning

confidence: 99%

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Do Interest Rate Liberalization and Fintech Mix? Impact on Shadow Deposits in China

Chan

2020

China and World Economy

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In this paper we attempt to characterize the stability of shadow deposits in China with interest rate liberalization and fintech developments. We emphasize that shadow banks provide higher but riskier returns and such characteristics affect the stability of shadow deposits. In our setting, the stability of shadow deposits is influenced by two offsetting effects, namely: the patience effect, which makes investors more willing to wait because of the potentially higher returns; and the uncertainty effect, which makes investors more likely to withdraw as a result of higher risk. Under liberalized interest rates, the patience effect will erode and the uncertainty effect will be heightened because the post‐liberalization higher return of traditional banks undermines the importance of the extra return of shadow deposits to depositors, while preserving the importance of the risk aspect. Fintech development is modeled as a reduction in the withdrawal cost that facilitates runs. This affects the stability of shadow deposits because of their wider fintech reliance. Regulators should be cautious in pushing interest rate liberalization and fintech application alongside building a safety net for shadow banking.

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The impact of Fintech on corporate carbon emissions: Towards green and sustainable development

Wang,

Zhao

et al. 2024

Bus Strat Env

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Fintech, as the fusion of finance and technology, has not only transformed the traditional financial industry and contributed to reshaping the real economy. But also, it holds the potential to offer a feasible solution for achieving green and sustainable development. This paper investigates the impact of Fintech on corporate carbon emissions (CCEs) by using data from the National Tax Survey Database (NTSD). The results suggest that Fintech development leads to a reduction in CCEs. Our findings remain robust even after using the instrumental variable approach to alleviate endogeneity problems. The mechanism analysis reveals that Fintech reduces CCEs via alleviating financing constraints, improving energy efficiency, and promoting green innovation. Heterogeneity analysis demonstrates that Fintech dramatically decreases CCEs from coal energy consumption, while increasing CCEs from consuming power and gas energy. Additionally, carbon emissions from state‐owned and foreign companies experience a more pronounced reduction through Fintech compared to those from private firms. Furthermore, firms in eastern and middle regions are more vulnerable to Fintech development. Moreover, enterprises in non‐high‐tech industries and high‐polluting industries exhibit noteworthy performance in reducing carbon emissions through Fintech adoption. This research offers policymakers a path to effectively govern CCEs and achieve their carbon reduction targets.

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Fintech, regulatory arbitrage, and the rise of shadow banks

Cited by 1,093 publications

References 41 publications

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

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

Do Interest Rate Liberalization and Fintech Mix? Impact on Shadow Deposits in China

The impact of Fintech on corporate carbon emissions: Towards green and sustainable development

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