Promising starting points include fraud detection and prevention, cybersecurity and information security monitoring, and internal controls.
As financial institutions evaluate where AI may create value, the conversation shouldn’t stop at AML/BSA, as addressed in a previous blog. There are other promising areas where institutions can improve how they operate and manage risk, but the right starting point is still outcomes, not tools.
Most institutions don’t need a long list of generic use cases. They need a few practical jumping-off points aligning to strategy, risk appetite, and program maturity. That is what helps organizations move forward with control instead of either freezing in place or trusting too much too quickly.
Promising starting points beyond AML/BSA
- Fraud detection and prevention
- Cybersecurity and information security monitoring
- Third-party and vendor risk management
- Operational risk and internal controls
Why these areas stand out for financial institutions
Fraud remains one of the clearest examples of where static rules and manual review struggle to keep pace with evolving activity. AI can help financial institutions identify anomalies, improve prioritization, and reduce noise so teams can focus on higher-risk cases.
Cybersecurity and information security monitoring are also strong candidates. Security teams face a high volume of alerts with varying levels of relevance. AI can help correlate activity across systems, users, and events, making it easier to identify meaningful indicators faster while still relying on established incident response disciplines.
Vendor risk management is increasingly important because AI is often entering organizations through vendors and vendors’ vendors. AI can help institutions review large volumes of documentation, surface dependency risks, and organize ongoing oversight more efficiently. Operational risk and internal controls can also benefit when AI helps connect exception trends, near misses, and recurring control failures across silos.
What institutions need before scaling
Data remains central across all of these areas. Financial institutions don’t need perfectly cleansed data to begin exploring value, but they do need to normalize the data they plan to use, understand whether it’s fit for purpose, and be realistic about the limitations of the inputs. Strong outcomes come less from perfect data and more from disciplined selection of use cases, well-understood sources, and clear validation of results.
Institutions also need visibility into current employee use of public generative AI tools and vendor-embedded AI. A stronger approach is to understand how AI is already entering the organization, establish acceptable use and cybersecurity guardrails, educate leadership and staff, and then prioritize opportunities aligning to strategy.
Keep the control side of experimentation in view
Human involvement remains essential across every promising starting point. AI can support prioritization, summarization, pattern recognition, and consistency, but people still need to own interpretation, escalation, customer context, and accountability. Controlled experimentation works well when the control side is explicit from the start.
That includes validation, vendor oversight, cybersecurity controls, data normalization for intended use, and change management helping teams understand how work is changing. The strongest institutions treat use cases as disciplined learning opportunities, not as a checklist of technology projects.
A practical way to think about next steps
The goal isn’t to automate everything or replace the people who make community financial institutions distinct. It’s to identify a few areas where AI may help the institution run better, reduce friction, improve insight, and support stronger decisions within a governed environment.
This post is part of a three-blog CLA series on moving forward securely with AI. Companion posts address the broader regulatory signal and the specific role of AI in AML/BSA programs.
How CLA can help financial institutions with AI
CLA helps financial institutions identify promising, strategy-aligned AI starting points, assess whether governance and control programs are ready to support them, and design-controlled experimentation balancing value, oversight, cybersecurity, and human judgment.