Figure 1: AI Patent Count, Number of Firms, AI Patent Rate, and AI Patent Ratio by Firm Type (2000-2020)
Note: The data comes from Lerner et al. (2024), the U.S. Patent and Trademark Office (PTO) Artificial Intelligence Patent Dataset, and U.S.
PTO Patentsview. For Panels A-C, the data consists of all artificial intelligence (AI) patents filed by financial innovators between 2000-2020 and granted by May 2025. For Panel D, the data consists of all patents (AI and non-AI) filed by financial innovators between 2000-2020 and granted by May
2025. Observations for these figures are at the level of firm type (bank, NBFI, nonfinancial company) and filing year. Panel A shows the number of AI patents. Panel B shows the number of firms. Panel C shows the AI patent rate, which is the number of AI patents divided by the number of firms. Panel
D shows the AI patent ratio, which is the number of AI patents divided by total patents.
The data comes from Lerner et al. (2024), the U.S. Patent and Trademark Office (PTO) Artificial Intelligence Patent Dataset, and U.S. PTO Patentsview. For Panels A-C, the data consists of all artificial intelligence (AI) patents filed by financial innovators between 2000-2020 and granted by May 2025. For Panel D, the data consists of all patents (AI and non-AI) filed by financial innovators between 2000-2020 and granted by May 2025. Observations for these figures are at the level of firm type (bank, NBFI, nonfinancial company) and filing year. Panel A shows the number of AI patents. The panel highlights the dominance of nonfinancial companies in AI patent counts from 2000 to 2020. Panel B shows the number of firms. The panel shows that the number of entities that are nonfinancial companies are higher than the number of banks and the number of NBFIs. Panel C shows the AI patent rate, which is the number of AI patents divided by the number of firms. The panel presents a more nuanced picture of the AI patent rate. While all firm types demonstrate an upward trend in AI patent rates, banks display the steepest growth, particularly in later years. Panel D shows the AI patent ratio, which is the number of AI patents divided by total patents. The panel shows that there is a general upward trend in the AI patent ratio over time for all firm types. Banks consistently have the highest AI patent ratio across all periods, followed by NBFIs. Nonfinancial companies have the lowest AI patent ratios.
Figure 2: AI Patent Concentration Measures by Firm Type (2000-2020)
Note: The data comes from Lerner et al. (2024), the U.S. Patent and Trademark Office (PTO) Artificial Intelligence Patent Dataset, and U.S. PTO Patentsview. The data consists of
all artificial intelligence (AI) patents filed by financial innovators between 2000-2020 and granted by May 2025. Observations for these figures are at the level of firm type (bank, NBFI, nonfinancial company) and filing year. Panel A shows the Herfindahl-Hirschman Index (HHI) of AI patents. Panel
B shows the Gini coefficient of AI patents.
The data comes from Lerner et al. (2024), the U.S. Patent and Trademark Office (PTO) Artificial Intelligence Patent Dataset, and U.S. PTO Patentsview. The data consists of all artificial intelligence (AI) patents filed by financial innovators between 2000-2020 and granted by May 2025. Observations for these figures are at the level of firm type (bank, NBFI, nonfinancial company) and filing year. Panel A shows the Herfindahl-Hirschman Index (HHI) of AI patents. The panel shows that the HHI for banks is higher than that of both nonfinancial companies and NBFIs. Panel B shows the Gini coefficient of AI patents. The panel presents an increasing trend in the Gini coefficient over time, pointing to growing disparities in AI patent ownership within each firm type.
Figure: AI Patent Rate – Subject Matter and Inventor Team Geographic Region (2000-2020)
Note: The data comes from Lerner et al. (2024), the U.S. Patent and Trademark Office (PTO) Artificial Intelligence Patent Dataset, and U.S. PTO Patentsview.
The data consists of all artificial intelligence (AI) patents filed by financial innovators between 2000-2020 and granted by May 2025. Observations for these figures are at the level of firm type (bank, NBFI, nonfinancial company) and filing year. Panel A shows the number of finance-related AI
patents divided by the number of firms. Panel B shows the number of planning and control AI patents divided by the number of firms. Panel C shows the number of AI patents with an inventor team from different geographic regions divided by the number of firms (i.e., multiple region AI patent
rate).
The data comes from Lerner et al. (2024), the U.S. Patent and Trademark Office (PTO) Artificial Intelligence Patent Dataset, and U.S. PTO Patentsview. The data consists of all artificial intelligence (AI) patents filed by financial innovators between 2000-2020 and granted by May 2025. Observations for these figures are at the level of firm type (bank, NBFI, nonfinancial company) and filing year. Panel A shows the number of finance-related AI patents divided by the number of firms. The panel reveals a notable increase in the finance-related AI patent rate for both banks and NBFIs, suggesting that they focus on AI innovation related to their business functions. This trend contrasts with the slower growth observed for nonfinancial companies in this domain. Panel B shows the number of planning and control AI patents divided by the number of firms. Panel C shows the number of AI patents with an inventor team from different geographic regions divided by the number of firms (i.e., multiple region AI patent rate). Panels B and C, focusing on planning and control AI patents and multi-region AI patents respectively, demonstrate a steeper increase in AI patent rate by banks than that of other firm types, particularly in the latter years of the study period.
Figure A.1: AI Patent Rate within Firm Type (2000-2020)
Note: The data comes from Lerner et al. (2024), the U.S. Patent and Trademark Office (PTO) Artificial Intelligence Patent Dataset, and U.S. PTO Patentsview. The data consists of all
artificial intelligence (AI) patents filed by financial innovators between 2000-2020 and granted by May 2025. Observations for these figures are at the level of firm sub-type and filing year. Panel A shows the number of AI patents divided by the number of firms by bank type (diversified banks,
regional banks) and filing year. Panel B shows the number of AI patents divided by the number of firms by NBFI type (asset management and custody, consumer finance, data processing and outsourced services, diversified capital markets, diversified real estate investment trusts (REITs), financial
exchanges and data, insurance brokers, investment banking and brokerage, life and health insurance, multi-line insurance, multi-sector holdings, property and casualty insurance, reinsurance, specialized finance, specialized REITs, and thrifts and mortgage finance) and filing year. Panel C shows the
number of AI patents divided by the number of firms by nonfinancial company type (communication services, consumer discretionary, consumer staples, energy, health care, industries, information technology, materials, real estate, utilities) and filing year.
The data comes from Lerner et al. (2024), the U.S. Patent and Trademark Office (PTO) Artificial Intelligence Patent Dataset, and U.S. PTO Patentsview. The data consists of all artificial intelligence (AI) patents filed by financial innovators between 2000-2020 and granted by May 2025. Observations for these figures are at the level of firm sub-type and filing year. Panel A shows the number of AI patents divided by the number of firms by bank type (diversified banks, regional banks) and filing year. The panel shows that the increase in bank AI patent rates is driven by diversified banks (large banks which offer a broad range of financial services) rather than smaller regional banks. Panel B shows the number of AI patents divided by the number of firms by NBFI type (asset management and custody, consumer finance, data processing and outsourced services, diversified capital markets, diversified real estate investment trusts (REITs), financial exchanges and data, insurance brokers, investment banking and brokerage, life and health insurance, multi-line insurance, multi-sector holdings, property and casualty insurance, reinsurance, specialized finance, specialized REITs, and thrifts and mortgage finance) and filing year. The top two NBFI groups are data processing and outsourced services (which consist of payment firms) and property and casualty insurance. Panel C shows the number of AI patents divided by the number of firms by nonfinancial company type (communication services, consumer discretionary, consumer staples, energy, health care, industries, information technology, materials, real estate, utilities) and filing year. The top three nonfinancial sectors are IT (including technology hardware, software, and semiconductor companies), communication services (including telecom and media companies), and consumer discretionary (including automobile, retail, and consumer services companies).
Figure A.2: Percentage of Most Impactful AI Patents by Firm Type
Note: The data comes from Lerner et al. (2024), the U.S. Patent and Trademark Office (PTO) Artificial Intelligence Patent Dataset, and U.S. PTO Patentsview. The data consists of all
artificial intelligence (AI) patents filed by financial innovators between 2000-2020 and granted by May 2025 for which breakthrough patent or novelty patent data is available. Observations for these figures are at the level of firm type and filing year. Panel A shows the percentage of breakthrough
AI patents. Breakthrough patents are defined by Kelly et al. (2021) as top 10 percent of patents with the highest ratios of forward similarity to backward similarity, indicating that they are dissimilar to prior patents but similar to future ones. The authors create the similarity measures based on
word frequency vectors. While the analysis in their paper goes to 2010, they extend the breakthrough indicator calculations to 2016 in their Github. Panel B depicts the percentage of patents that are in the top 25 percent of patents with the highest ratios of forward similarity to backward
similarly, as defined by Arts et al. (2021). The authors use a cosine similarity measure that takes into account the combination of keywords and their frequencies. They define their measure for all patents granted by May 2018.
The data comes from Lerner et al. (2024), the U.S. Patent and Trademark Office (PTO) Artificial Intelligence Patent Dataset, and U.S. PTO Patentsview. The data consists of all artificial intelligence (AI) patents filed by financial innovators between 2000-2020 and granted by May 2025 for which breakthrough patent or novelty patent data is available. Observations for these figures are at the level of firm type and filing year. Panel A shows the percentage of breakthrough AI patents. Breakthrough patents are defined by Kelly et al. (2021) as top 10 percent of patents with the highest ratios of forward similarity to backward similarity, indicating that they are dissimilar to prior patents but similar to future ones. The authors create the similarity measures based on word frequency vectors. While the analysis in their paper goes to 2010, they extend the breakthrough indicator calculations to 2016 in their Github. Panel B depicts the percentage of patents that are in the top 25 percent of patents with the highest ratios of forward similarity to backward similarly, as defined by Arts et al. (2021). The authors use a cosine similarity measure that takes into account the combination of keywords and their frequencies. They define their measure for all patents granted by May 2018. Both panels suggest that the proportion of most impactful patents for banks are similar to those for NBFIs and nonfinancial companies, implying that banks are not simply following other entities’ innovations to advance their technology.
Figure A.3: Finance-Related AI Patent Rate within Firm Type
Note: The data comes from Lerner et al. (2024), the U.S. Patent and Trademark Office (PTO) Artificial Intelligence Patent Dataset, and U.S. PTO Patentsview. The data consists of all
artificial intelligence (AI) patents filed by financial innovators between 2000-2020 and granted by May 2025. Observations for these figures are at the level of finance category (accounting, asset management, banking, credit, insurance, payments, tax strategies, and trading) and filing year,
restricted by firm type depending on the panel. Panel A shows the number of finance-related AI patents divided by the number of firms by finance category for banks. Panel B shows the number of finance-related AI patents divided by the number of firms by finance category for NBFIs. Panel C shows the
number of finance-related AI patents divided by the number of firms by finance category for nonfinancial companies.
The data comes from Lerner et al. (2024), the U.S. Patent and Trademark Office (PTO) Artificial Intelligence Patent Dataset, and U.S. PTO Patentsview. The data consists of all artificial intelligence (AI) patents filed by financial innovators between 2000-2020 and granted by May 2025. Observations for these figures are at the level of finance category (accounting, asset management, banking, credit, insurance, payments, tax strategies, and trading) and filing year, restricted by firm type depending on the panel. Panel A shows the number of finance-related AI patents divided by the number of firms by finance category for banks. Panel B shows the number of finance-related AI patents divided by the number of firms by finance category for NBFIs. Panel C shows the number of finance-related AI patents divided by the number of firms by finance category for nonfinancial companies. Panels A-C show that within the set of finance-related AI patents, the rate related to payment architectures, schemes, and protocols is the highest for all firm types.
Figure A.4: AI Component Patent Rate within Firm Type
Note: The data comes from Lerner et al. (2024), the U.S. Patent and Trademark Office (PTO) Artificial Intelligence Patent Dataset, and U.S. PTO Patentsview. The data consists of all artificial
intelligence (AI) patents filed by financial innovators between 2000-2020 and granted by May 2025. Observations for these figures are at the level of AI component (evolutionary computation, AI hardware, knowledge processing, machine learning (ML), natural language processing (NLP), speech, and
computer vision) and filing year. Panel A shows the number of AI patents divided by the number of firms by AI component category for banks. Panel B shows the number of AI patents divided by the number of firms by AI component category for NBFIs. Panel C shows the number of AI patents divided by the
number of firms by AI component category for nonfinancial companies.
The data comes from Lerner et al. (2024), the U.S. Patent and Trademark Office (PTO) Artificial Intelligence Patent Dataset, and U.S. PTO Patentsview. The data consists of all artificial intelligence (AI) patents filed by financial innovators between 2000-2020 and granted by May 2025. Observations for these figures are at the level of AI component (evolutionary computation, AI hardware, knowledge processing, machine learning (ML), natural language processing (NLP), speech, and computer vision) and filing year. Panel A shows the number of AI patents divided by the number of firms by AI component category for banks. Panel B shows the number of AI patents divided by the number of firms by AI component category for NBFIs. Panel C shows the number of AI patents divided by the number of firms by AI component category for nonfinancial companies. Across all panels, planning and control AI patents are top contributors to patent rate for all firm types, especially for financial firms.
Figure A.5: Inventor Geography AI Patent Rate within Firm Type
Note: The data comes from Lerner et al. (2024), the U.S. Patent and Trademark Office (PTO) Artificial Intelligence Patent Dataset, and U.S. PTO Patentsview. The data consists of all
artificial intelligence (AI) patents filed by financial innovators between 2000-2020 and granted by May 2025. Observations for these figures are at the level of inventor team geography (all in west U.S., all in south U.S., all in midwest U.S., all in northeast U.S., all in foreign countries, or
multi-region team) and filing year. Panel A shows the number of AI patents divided by the number of firms by inventor team geography for banks. Panel B shows the number of AI patents divided by the number of firms by inventor team geography for NBFIs. Panel C shows the number of AI patents divided
by the number of firms by inventor team geography for nonfinancial companies.
The data comes from Lerner et al. (2024), the U.S. Patent and Trademark Office (PTO) Artificial Intelligence Patent Dataset, and U.S. PTO Patentsview. The data consists of all artificial intelligence (AI) patents filed by financial innovators between 2000-2020 and granted by May 2025. Observations for these figures are at the level of inventor team geography (all in west U.S., all in south U.S., all in midwest U.S., all in northeast U.S., all in foreign countries, or multi-region team) and filing year. Panel A shows the number of AI patents divided by the number of firms by inventor team geography for banks. Panel B shows the number of AI patents divided by the number of firms by inventor team geography for NBFIs. Panel C shows the number of AI patents divided by the number of firms by inventor team geography for nonfinancial companies. Across all panels, multi-region AI patents are top contributors to patent rate for all firm types, especially for financial firms.