Does FinTech Innovation Improve Traditional Banks' Efficiency and Risk Measures? A New Methodology and New Machine-Learning-Based Evidence from Patent Filings

53 Pages Posted: 7 Feb 2023 Last revised: 30 Sep 2023

See all articles by Libing Fang

Libing Fang

Nanjing University - School of Management and Engineering

Xindan Li

Nanjing University

Avanidhar Subrahmanyam

University of California, Los Angeles (UCLA) - Finance Area; Financial Research Network (FIRN)

Ke Zhang

Nanjing University - School of Management and Engineering

Date Written: February 7, 2023

Abstract

We develop a new bank-specific measure of FinTech to test how such innovation impacts traditional
banks’ operational efficiency and risk. Our methodology goes allin machine learning
to measure FinTech innovation within Chinese commercial (traditional) banks. Using
propensity score matching and difference-in-differences, we show that FinTech significantly
improves banks’return on assets, as well as cost and income efficiency. FinTech also ameliorates
banks’ risk measures—including overall risk (Z score), and the capital asset, liquidity,
and nonperforming loan ratios. FinTech has a greater positive impact on efficiency and risk
for banks with greater labor intensity and higher managerial ability.

Keywords: Banks, FinTech, loans, deposits

JEL Classification: G21, G18

Suggested Citation

Fang, Libing and Li, Xindan and Subrahmanyam, Avanidhar and Zhang, Ke, Does FinTech Innovation Improve Traditional Banks' Efficiency and Risk Measures? A New Methodology and New Machine-Learning-Based Evidence from Patent Filings (February 7, 2023). Available at SSRN: https://ssrn.com/abstract=4350734 or http://dx.doi.org/10.2139/ssrn.4350734

Libing Fang (Contact Author)

Nanjing University - School of Management and Engineering ( email )

Nanjing, 210093
China

Xindan Li

Nanjing University ( email )

Nanjing, Jiangsu 210093
China

Avanidhar Subrahmanyam

University of California, Los Angeles (UCLA) - Finance Area ( email )

Los Angeles, CA 90095-1481
United States
310-825-5355 (Phone)
310-206-5455 (Fax)

Financial Research Network (FIRN)

C/- University of Queensland Business School
St Lucia, 4071 Brisbane
Queensland
Australia

Ke Zhang

Nanjing University - School of Management and Engineering ( email )

Nanjing, 210093
China

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