Responsible AI Management in Financial Regulation: Organizational Capabilities and Governance in FinTech Adoption

Financial authorities are deploying artificial intelligence in supervisory technology, yet operational adoption remains sharply uneven. This study asks which dimensions of organizational readiness differ most between advanced economies and emerging market and developing economies. SupTech is treated as the regulatory branch of the FinTech ecosystem. A comparative secondary analysis used published tabulations from the State of SupTech Report 2025. Operational AI adoption was defined as limited deployment, widespread deployment, or full integration within SupTech applications. Readiness indicators covered senior-endorsed strategy, cloud infrastructure, internal training, formal data-science organization, and multilateral collaboration. Contingency counts were reconstructed for the two economic groups. Comparisons used percentage-point gaps, odds ratios, 95% confidence intervals, and two-sided Fisher exact tests. Benjamini-Hochberg adjustment addressed multiple comparisons, and a plus-or-minus-one-count analysis tested sensitivity to rounded percentages. Operational AI adoption was 53.0 percentage points higher among advanced-economy authorities (68.0%, 17/25) than among emerging-market and developing-economy authorities (15.0%, 16/107; OR = 12.09, 95% CI [4.47, 32.66], adjusted p < 0.001). Internal training differed by 26.6 points (OR = 3.68, adjusted p = 0.015), and multilateral collaboration differed by 32.1 points (OR = 3.86, adjusted p = 0.006). The absence of formal data-science capabilities was 30.7 points more common in the emerging-market and developing-economy group (adjusted p = 0.006). All four contrasts remained significant in the rounding sensitivity test. Differences in senior-endorsed strategy and cloud adoption were smaller and statistically indistinguishable. The largest disparities therefore concern workforce development, formalized analytical capacity, and international cooperation rather than strategy or cloud infrastructure. These findings identify organizational readiness gaps relevant to responsible AI governance in financial supervision. Because the source provides aggregate tabulations, the analysis does not support institution-level or causal inference.