Key views of Primary concerns of AI adoption in risk and compliance across FIs and vendors:
- Global State of RegTech report surveyed 300 FI decision-makers and 100 vendors on their main concerns about AI use in risk and compliance
- Transparency and explainability shows the widest gap, cited by 43% of institutions but 72% of vendors
- The data points to a sector where AI adoption is advancing but governance, explainability and model quality remain unresolved challenges
Global State of RegTech report surveyed 300 FI decision-makers and 100 vendors on their main concerns about AI use in risk and compliance
The report was produced by RegTech Analyst and Parker & Lawrence Research.
It draws on global surveys of 300 senior risk and compliance decision-makers at financial institutions and 100 RegTech vendors, supplemented by qualitative interviews with regulators, regulated entities and market experts.
The study incorporates deep-dive analysis across six risk and compliance domains and bottom-up market sizing built from 2026 spend data, population modelling and a review of 63 published market estimates.
Among the questions put to both groups was what they consider to be the main concerns regarding the use of AI in risk and compliance.
Transparency and explainability shows the widest gap, cited by 43% of institutions but 72% of vendors
The results reveal broad agreement on the top concerns, though vendors consistently perceive AI risk as more acute than institutions report.
Multiple responses were permitted.
Model performance and reliability ranked first among institutions at 51%, with vendors placing it even higher at 58%.
AI governance and control frameworks were cited equally by both groups at 47%.
Transparency and explainability was the third concern for institutions at 43%, but the single biggest worry for vendors at 72%, the largest gap in the entire dataset.
Regulatory compliance and supervisory expectations followed at 41% among institutions and 55% among vendors.
Data leakage and misuse stood at 36% and 35% respectively, cyber threats and attack surface at 31% and 33%, and data readiness at 25% among institutions and 37% among vendors.
Clear ROI was cited by 19% of institutions and 23% of vendors, internal expertise by 16% and 35%, and ethics and reputational risk by 13% and 12%.
The data points to a sector where AI adoption is advancing but governance, explainability and model quality remain unresolved challenges
The 72% vendor figure for transparency and explainability stands out sharply against the 43% recorded by institutions.
Vendors appear acutely aware that black-box AI is difficult to defend in a regulatory context, and that explainability will increasingly be a condition of deployment rather than an optional feature.
The gap on internal expertise is similarly telling: only 16% of institutions flag it as a concern, while 35% of vendors do, suggesting that vendors have a clearer view of the skills gap than their clients currently acknowledge.
What the data collectively signals is that AI adoption in risk and compliance is not being held back by a lack of appetite.
The concerns are more specific and more technical, centred on model quality, governance structures and the ability to demonstrate to regulators that AI-driven decisions can be understood, challenged and defended.
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