Patent Quality, Institutions, and Innovation Leadership in FinTech: A Multidimensional Machine Learning Analysis

TY - JOUR

T1 - Patent Quality, Institutions, and Innovation Leadership in FinTech

T2 - A Multidimensional Machine Learning Analysis

AU - Sahiner, Mehmet

AU - Karavidas, Dionysios

AU - Chotisarn, Noptanit

AU - Dehghani, Milad Armani

N1 - Publisher Copyright: © 2026 IEEE.

PY - 2026/4/2

Y1 - 2026/4/2

N2 - Firms in technology-intensive industries require reliable tools to evaluate patent quality and guide R&D resource allocation. Conventional indicators such as patent counts or citation totals provide incomplete signals and often privilege volume over strategic substance. This study develops and applies a patent quality index (PQI), a multidimensional framework integrating technological scope, legal robustness, and market responsiveness to capture the intrinsic quality of patents. Using a global dataset of 96 657 FinTech patents filed between 2000 and 2023, the PQI is constructed through complementary statistical and machine-learning methods and applied to compare patent quality across firms and jurisdictions. Results reveal substantial heterogeneity in patent quality, clear tradeoffs between portfolio size and average quality at the firm level, and systematic cross-country differences. Specifically, regulatory quality, corruption control, and foreign direct investment significantly shape national patterns of patent quality, linking firm-level innovation strategies with broader governance environments. By providing a scalable and theory-grounded measure of patent quality, the PQI advances innovation measurement and offers practical guidance for managers, policymakers, and investors seeking to benchmark portfolios, prioritize high-value patents, and design policies that promote substantive innovation. Although demonstrated in the FinTech sector, the framework is adaptable to other technology-driven industries.

AB - Firms in technology-intensive industries require reliable tools to evaluate patent quality and guide R&D resource allocation. Conventional indicators such as patent counts or citation totals provide incomplete signals and often privilege volume over strategic substance. This study develops and applies a patent quality index (PQI), a multidimensional framework integrating technological scope, legal robustness, and market responsiveness to capture the intrinsic quality of patents. Using a global dataset of 96 657 FinTech patents filed between 2000 and 2023, the PQI is constructed through complementary statistical and machine-learning methods and applied to compare patent quality across firms and jurisdictions. Results reveal substantial heterogeneity in patent quality, clear tradeoffs between portfolio size and average quality at the firm level, and systematic cross-country differences. Specifically, regulatory quality, corruption control, and foreign direct investment significantly shape national patterns of patent quality, linking firm-level innovation strategies with broader governance environments. By providing a scalable and theory-grounded measure of patent quality, the PQI advances innovation measurement and offers practical guidance for managers, policymakers, and investors seeking to benchmark portfolios, prioritize high-value patents, and design policies that promote substantive innovation. Although demonstrated in the FinTech sector, the framework is adaptable to other technology-driven industries.

KW - Fintech

KW - innovation management

KW - intellectual property

KW - machine learning

KW - patent quality

KW - R&D strategy

UR - https://www.scopus.com/pages/publications/105034867900

U2 - 10.1109/TEM.2026.3680345

DO - 10.1109/TEM.2026.3680345

M3 - Article

AN - SCOPUS:105034867900

SN - 0018-9391

VL - 73

SP - 2666

EP - 2681

JO - IEEE Transactions on Engineering Management

JF - IEEE Transactions on Engineering Management

ER -