FEDERAL RESERVE BANK

OF PHILADELPHIA

Fintech Fintech Lending and the Roles of Alternative Data

CUNY PhD Seminar in Applied EconomicsSeptember 22, 2020

Julapa JagtianiSenior Economic Advisor and Economist

The views expressed here are my own and not the views of the FRB Philadelphia or the Federal Reserve Systems

Fintech Background

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Fintech Broadly Defined Data aggregation, open banking, application programming interface (API),

Artificial intelligence (AI), machine learning (ML), Big data, alternative data

Natural language processing (NLP), Regtech, Suptech, InsureTech, Roboadvisors

Crowdfunding, Peer-2-Peer (P2P), marketplace lending (MPL)

Bitcoins, cryptocurrencies, Libra, e-wallet, Venmo, initial coin offerings (ICOs), decentralized finance (DeFi), blockchain, other DLTs, smart contracts

Cloud computing, Quantum computing

Real-Time Payments and Settlements, Central Bank Digital Currency (CBDC)

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Big data and AI/ML – risk associated with data accuracy, representativeness, interpretability, What is inside the black box?

Data sharing – concerns related to consumer privacy, fairness/biased against certain groups, cyber security.

Cryptocurrencies -- price volatility – Bitcoin at $20,000 in Dec 2017; $3,187 in Dec 2018, and $11,711 as of August 2020 -- unrelated to stock performance?

Real-time payment -- private vs. public digital currencies? Would central bank digital currency (CBDC) solve the volatility and public trust issues?

Cloud Computing -- Big Tech (Google, AWS, Microsoft, IBM) provide cloud computing service & technology – enabler of the Fintech ecosystem but very concentrated mkt – implications on interconnectedness, financial stability, cyber security, etc.

Example of Potential Research Questions 4

Fintech Lending and Alternative Data

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Various Business Models & Products Offered

Marketplace Lender

P2P but recently some also securitize their loans (Fintech ABS)

Balance Sheet Lender

Ease of application, reduced collateral requirements, and expedited funding

Payment Processors

Using proprietary big data collected while processing payment transactions

Also use SBL as a means to grow business volume

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Nontraditional data was increasingly used in credit decisions, allowing for more accurate risk evaluation and risk pricing – resulting in more

borrowers accessing credit at lower cost

Personal Loans Small Business Loans

Sources: Jagtiani and Lemieux (2019)

Note: Hughes, Jagtiani, and Moon (2019) find that LendingClub became more efficient than traditional lenders of the same size (2013 vs. 2016).

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The Potential Enhanced Risk Assessment and Pricing

Source: Jagtiani and Lemieux (2019)

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Delinquency Rate of Fintech Loansby Rating Grades and Year of Origination

Source: Jagtiani and Lemieux (2019) Source: Croux, Jagtiani, Korivi, and Vulanovic (2020)

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The Potential Lower Funding Costs to ConsumersBy Rating Grades, FICO Scores, and Maturity

Source: Croux, Jagtiani, Korivi, and Vulanovic (2020)

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Source: Jagtiani and Lemieux (2019)

Fintech Lending and Consumer Credit Access

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More Empirical Research on Credit Access

"Does FinTech Substitute for Banks? Evidence from the Paycheck Protection Program" by C.A. Dice and J. Lebbersohn (2020) working paper Ohio State and UC Irvine

SBL -- They study the response of FinTech and nonbank lenders to financial services demand created by the introduction of the PPP. Online banks and nonbank financial institutions are disproportionately used in ZIP codes located with fewer bank branches, and in industries with little ex ante small-business lending. “Borrowers were more likely to get a FinTech-enabled loan if they are located in ZIP codes where local banks were unlikely to originate PPP loans.”

This is consistent with Jagtiani and Lemieux (2018) “Do Fintech Lenders Penetrate Areas That Are Underserved by Traditional Banks?” Journal of Economics and Business 100, November‒December 2018, 43‒54.

Personal -- We use account-level data from LendingClub and Y-14M data CCAR data -- and we find that LendingClub’s consumer lending activities penetrated areas that were underserved by traditional banks – 50% in highly concentrated markets; 40% where bank branches decline >5%; 60% where there are <30 bank branches per 100,000 people (fewer bank branches per capita). We also find that the portion of LendingClub loans increases in areas where the local economy is not performing well (based on UE, HPA, Income).

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More Empirical Research on Credit Access

Jagtiani, Lambie-Hanson, and Lambie-Hanson (2019) “Fintech Lending and Mortgage Credit Access.” FRB Philadelphia RWP #19-47, November.

Mortgages – We use HMDA data and find more Fintech mortgage originations in areas that had larger rate of mortgage denials by non-Fintech lenders in the previous period. In addition, fintechlenders have greater market shares in areas with lower average credit scores.

Distribution of Conventional Mortgage Offers by

VantageScore Segments, 2016-2019

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Allen, Franklin, Xian Gu, and Julapa Jagtiani (2020) “A Survey of Fintech Research and Policy Discussion,” FRB Philadelphia RWP#20-21

Croux, Chris, Julapa Jagtiani, Tarunsai Korivi, and Milos Vulanovic (2020) “Important Factors Determining Fintech Loan Default: Evidence from LendingClub Consumer Platform,” Journal of Economic Behavior & Organization, Volume 173, May, 270-296.

Goldstein, Itay, Julapa Jagtiani, and Aaron Klein (2019) “Fintech and the New Financial Landscape.” Bank Policy Institute (BPI): Banking Perspectives, Q1:7, March.

Hughes, Joe, Julapa Jagtiani, and Choon G. Moon (2019) “Consumer Lending Efficiency: Commercial Banks Versus a Fintech Lender,” FRB Philadelphia RWP #19-22 (April)

Additional Fintech Readings by J. Jagtiani (1)16

Jagtiani, Julapa, Michael Papaioannou, George Tsetsekos, Erik Dolson, and Dan Milo (2020) “Cryptocurrencies: Regulatory Perspectives and Implications for Investors.” in Handbook of Alternative Finance, edited by Raghavendra Rau, Robert H. Wardrop, and Luigi Zingales, Palgrave MacMillan, forthcoming.

Jagtiani, Julapa, and Cathy Lemieux (2019) “The Roles of Alternative Data and Machine Learning in Fintech Lending: Evidence from LendingClub Consumer Platform.” Financial Management Winter 2019, 48(4), 1009‒1029.

Jagtiani, Julapa, Lauren Lambie-Hanson, and Tim Lambie-Hanson (2019) “Fintech Lending and Mortgage Credit Access.” FRB Philadelphia RWP #19-47, November.

Jagtiani, Julapa, Michael Papaioannou, and George Tsetsekos (2019) “Cryptocurrencies in the Global Economy.” The Journal of Investing. Cryptocurrency Special Issue 28 (3), April, 7‒12.

Additional Fintech Readings by J. Jagtiani (2)17

Jagtiani, Julapa, Todd Vermilyea, and Larry Wall (2018) “The Roles of Big Data and Machine Learning in Bank Supervision.” The Clearing House: Banking Perspectives, Quarter 1, 6(1), 44‒51.

Jagtiani, Julapa and Kose John (2018) “Fintech – The Impact on Consumers and Regulatory Responses,” Journal of Economics and Business , Volume 100, November-December 2018, 1-6.

Jagtiani, Julapa and Cathy Lemieux (2018) “Do Fintech Lenders Penetrate Areas That Are Underserved by Traditional Banks?” Journal of Economics and Business 100, November‒December 2018, 43‒54.

Dolson, Erik and Julapa Jagtiani (2020) “Are Fintech Lenders Reaching Out to Undeserved Consumers?” work in progress, FRB Philadelphia RWP (forthcoming in October 2020).

Additional Fintech Readings by J. Jagtiani (3)18

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