Korim Index · Monthly analysis · Updated
October’s financial services question is not whether AI needs oversight, but what a bank should pay to make that oversight workable. The commercial opening is in turning governance into a usable operating process: identifying third-party AI services, assigning responsibility and collecting evidence that supports an approval decision. For suppliers, the strongest offer is not another policy document. It is a focused service that reduces duplicated reviews and gives risk teams a clearer view of what they are approving.
Top opportunities this month
- Turn third-party AI approvals into a focused assessment service
Target mid-sized banks’ risk functions, with procurement and model oversight involved from the outset.
- Build recurring assurance around operational AI controls
Focus on approved AI workflows where risk and technology teams need continuing oversight.
- Prioritise high-risk supplier relationships before scaling governance
For US community banks, start with a ranked view of AI-related supplier exposure.
Korim's view
Sell the decision support before the platform. Start with one AI service, one accountable owner and a defined approval route. Establish what evidence the bank needs, which checks already exist and where work is being duplicated. That creates a clearer investment test than a broad promise to transform governance. Framework design is the entry point; repeatable monitoring and independent testing are the next commercial steps. Judge each step by its operational value, not the volume of documentation produced. For investors and partners, the differentiator is a supplier’s ability to fit existing risk processes while making AI-specific controls easier to apply.
What changed this month
Proportionate oversight is the commercial priority
The current Index focus brings board-approved third-party AI frameworks into the buying conversation. Recent research explains why a single governance package is the wrong starting point. EY’s April survey of 17 Nordic banks found broader, more enterprise-structured risk frameworks at large banks and simpler practices at small and medium-sized banks. The lesson for suppliers is segmentation: design around the customer’s operating capacity rather than the largest institution’s control model. September reporting from KPMG describes proposed US guidance that would tailor third-party risk management to assessed risk and prioritise higher-risk relationships. Its community-bank provisions address reliance on third parties, particularly core service providers. This is proposed guidance, not a new binding obligation. The direction nevertheless reinforces a practical sales proposition: concentrate effort where supplier exposure and operational consequences are greatest.
Where the opportunity lies
Turn third-party AI approvals into a focused assessment service
Target mid-sized banks’ risk functions, with procurement and model oversight involved from the outset. Offer a fixed-scope assessment of one third-party AI service rather than a wholesale framework replacement. The useful deliverables are named responsibilities, a supplier evidence checklist, mapped gaps and a clear approval route. Price and scope the engagement around work the bank needs completed, with implementation as a separate next step. The Nordic research supports tailoring the offer to institutional capacity; it does not measure demand across the wider banking market. The commercial discipline is to show which existing reviews the service simplifies and which genuinely new checks it adds.
Build recurring assurance around operational AI controls
Focus on approved AI workflows where risk and technology teams need continuing oversight. Deloitte’s June analysis argues for AI-specific monitoring, accountability and controls alongside existing governance. It cites a survey in which only 21% of organisations had a mature governance model for autonomous AI agents. That is a cross-organisational result, not a banking maturity rate. Translate the issue into a paid pilot with agreed access to logs, exception reviews and escalation records. Define useful results before discussing a recurring contract. A service built around repeat testing and evidence maintenance has a clearer renewal rationale than periodic policy rewriting. The pilot should demonstrate how the bank detects and handles control failures, not simply generate an audit trail.
Prioritise high-risk supplier relationships before scaling governance
For US community banks, start with a ranked view of AI-related supplier exposure. Third-party risk teams need to distinguish relationships that warrant deeper assessment from those that fit existing checks. Offer a focused advisory review, then assess whether evidence maintenance merits an ongoing service. Build the scope around access to supplier documentation, visibility into subcontractors and dependence on core providers. KPMG’s account of proposed guidance supports this proportionate approach without establishing a settled compliance requirement. The related Index opening is a governance supply bottleneck: specialists who make reviews repeatable and manageable are better positioned than providers selling the same checklist for every relationship.
What could weaken the case
- Regulatory attention does not establish a budget. Proposed US guidance may change, and the Nordic sample cannot be used as a global banking market estimate. Avoid selling a universal obligation that the evidence does not support.
- Banks may already have tools and staff that cover the relevant risks. Integration costs, restricted evidence access or extra approval work can erase the value of a separate provider.
- Independent assurance needs a clear boundary. A supplier that designs controls and then assesses them must disclose the overlap and agree how findings will be challenged.
- Recurring revenue is not guaranteed. Repeated one-off policy purchases, weak pilot results or no measurable reduction in review effort would weaken the monitoring and evidence-maintenance case.
What to watch next
- Paid assessments with a named budget owner, a defined AI service and access to operational evidence.
- Pilots converting into repeat testing or monitoring contracts, with agreed measures of review effort and exception handling.
- Changes to the proposed US guidance, particularly proportionality and the treatment of core-provider relationships.
- Evidence that customers are replacing duplicated checks rather than adding another layer of governance work.
Research behind the analysis
EY’s survey of 17 Nordic banks found that large banks operate broader, more enterprise-structured risk frameworks, while small and medium-sized banks have simpler practices across non-financial, ESG and AI risk management.
EY ·Deloitte reports that midmarket banks need AI-specific monitoring, accountability and controls alongside existing governance frameworks, citing its survey finding that only 21% of organisations have a mature governance model for autonomous AI agents.
Deloitte ·KPMG reports that proposed US banking guidance would tailor third-party risk management to risk assessments and prioritise higher-risk relationships, with supplemental guidance addressing community banks’ reliance on third parties, particularly core service providers.
KPMG ·