AI for Financial Services: Risk, Compliance & Automation
Financial teams review large volumes of transactions, documents and reports under strict rules. AI agents can do the first pass: flag unusual activity, check documents against requirements and prepare summaries, so specialists spend their time on the cases that matter.
This is an example project. It shows how we would approach this kind of work in financial services; it does not describe a specific client. Results depend on your process and data, and we agree on how to measure them with you before starting.
Common Challenges
- Manual reviews of transactions and documents take time and are hard to scale.
- Rule-based alerts produce many false alarms that teams must clear by hand.
- Compliance checks and reporting involve repetitive data gathering.
- Every decision must be explainable and auditable.
Our Approach
We connect Sentinel and Orbis agents to your transaction and document data. They score activity for unusual patterns, pre-check documents against your requirements and prepare review packs, while every flag and decision is logged and final decisions stay with your analysts.
- Unusual-activity detection that prioritises cases for review.
- Document checks against KYC and onboarding requirements.
- Automated preparation of compliance and management reports.
- Full audit trail of what the agent checked and why.
Agents used: Sentinel cybersecurity monitoring agent, Orbis business analytics agent
Typical Rollout
Discovery
Map the current process, systems and data, agree on the baseline numbers and how success will be measured.
Pilot build
Build and connect the agent for one scope: one review queue or one document type. Set permissions, guardrails and hand-off rules.
Supervised pilot
Run on real cases with a person reviewing the agent's work, tuning it until results are reliable.
Review and scale
Compare results with the baseline and extend to more teams, sites or tasks only if the pilot clearly pays off.
Timings are indicative and depend on your systems and scope.
What We Measure
We record these before the pilot starts and compare them afterwards, so you can see whether it worked.
- Review time per case
- False alarm rate
- Backlog of pending reviews
- Time to prepare recurring reports
Explore This for Your Business
Tell us about one process that takes too much of your team's time. We'll tell you honestly whether AI can help and what a pilot would look like.