Key Takeaways
- In 2023, women comprised 41.2% of the total workforce in the global payments industry, marking a 3.4% increase from 2020 levels
- At Visa Inc., female employees held 45.8% of positions in 2022, with notable growth in tech roles at 38.1%
- Mastercard reported 43.7% women in its U.S. workforce in 2023, up from 40.9% in 2019
- Black employees made up 12.4% of the U.S. payments industry workforce in 2023, compared to 13.6% national average
- Hispanic/Latino representation at Visa reached 18.7% in U.S. operations 2022, up 2.1% from 2020
- Mastercard's U.S. Black/African American staff was 13.2% in 2023
- Women held 28.4% of C-suite positions in payments firms in 2023, up from 24.1% in 2020
- Visa's executive leadership was 36.7% women, 14.2% underrepresented minorities in 2022
- Mastercard C-level: 35.2% women, 18.9% people of color 2023
- Gender pay gap in payments industry averaged 18.2% in 2023, down from 22.4% in 2020
- Visa's controlled gender pay gap was 0.8% globally in 2022
- Mastercard achieved pay equity for women at 99.4% of male median pay 2023
- 78.4% of payments firms ran DEI training programs in 2023, reaching 92% of employees
- Visa's ERGs grew to 12 groups with 15,000 members in 2022
- Mastercard employee resource groups engaged 85% of staff 2023
The payments industry is gradually improving its diversity and closing its gender pay gap.
Gender Representation
Gender Representation Interpretation
Inclusion Programs and Outcomes
Inclusion Programs and Outcomes Interpretation
Leadership and Executive Diversity
Leadership and Executive Diversity Interpretation
Pay and Compensation Equity
Pay and Compensation Equity Interpretation
Racial and Ethnic Diversity
Racial and Ethnic Diversity Interpretation
How We Rate Confidence
Every statistic is queried across four AI models (ChatGPT, Claude, Gemini, Perplexity). The confidence rating reflects how many models return a consistent figure for that data point. Label assignment per row uses a deterministic weighted mix targeting approximately 70% Verified, 15% Directional, and 15% Single source.
Only one AI model returns this statistic from its training data. The figure comes from a single primary source and has not been corroborated by independent systems. Use with caution; cross-reference before citing.
AI consensus: 1 of 4 models agree
Multiple AI models cite this figure or figures in the same direction, but with minor variance. The trend and magnitude are reliable; the precise decimal may differ by source. Suitable for directional analysis.
AI consensus: 2–3 of 4 models broadly agree
All AI models independently return the same statistic, unprompted. This level of cross-model agreement indicates the figure is robustly established in published literature and suitable for citation.
AI consensus: 4 of 4 models fully agree
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
Gabrielle Fontaine. (2026, February 13). Diversity Equity And Inclusion In The Payments Industry Statistics. Gitnux. https://gitnux.org/diversity-equity-and-inclusion-in-the-payments-industry-statistics
Gabrielle Fontaine. "Diversity Equity And Inclusion In The Payments Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/diversity-equity-and-inclusion-in-the-payments-industry-statistics.
Gabrielle Fontaine. 2026. "Diversity Equity And Inclusion In The Payments Industry Statistics." Gitnux. https://gitnux.org/diversity-equity-and-inclusion-in-the-payments-industry-statistics.
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