From filing to grant: Predicting patent outcomes in FinTech using a predictive analytics perspective

Patents are critical indicators of innovation, especially in fast-evolving domains like Financial Technology (FinTech). However, accurately predicting patent grant outcomes with modern artificial intelligence techniques has remained challenging. This study addresses that gap by applying state-of-the-art machine learning (ML), including ensemble methods and deep learning models, to a dataset of 20,008 FinTech patent applications from 2000 to 2020. We demonstrate that our ML framework can forecast grant success with high precision (up to 89 %), revealing that patent quality and strategic filing choices, such as optimal IPC classes and jurisdictions, are key determinants of grant probability. The findings highlight practical implications for innovators and intellectual property managers, such as better resource allocation and informed patent strategy decisions. Overall, this work introduces a novel, AI-driven approach to patent analytics in FinTech, offering a forward-looking tool to enhance innovation management and strategic IP planning. • ML framework predicts FinTech patent grants with up to 89 % classification precision. • ● Patent quality, not volume, drives grant success; IPC and location also matter. • ● US leads in FinTech patent grants (∼82 %); global variation aids IP strategy. • ● Offers AI tool for patent offices to boost efficiency and support innovation.