AI your examiner, your model risk team and your board can all sign
Banks, insurers and asset managers do not have an AI idea problem. They have a problem getting an idea through model risk management, fair-lending review and the board without it losing its economics along the way.
The pressure
What is forcing the pace in financial services
- Cost-to-income pressure is pushing document-heavy operations — intake, KYC refresh, servicing, claims — toward automation faster than governance capacity is growing.
- Fintech and direct-to-consumer entrants have reset customer expectations on turnaround time in lending, onboarding and claims.
- Boards are asking for a single funded AI roadmap instead of a portfolio of departmental pilots nobody can compare.
The constraint
What the work has to satisfy
- Model risk management expectations (SR 11-7 and equivalents) apply to AI systems that influence credit, pricing, capital or reserving — including systems the business did not think of as models.
- Adverse-action and fair-lending obligations require reasons that a human can give a customer, which rules out unexplainable scoring in the decision path.
- BSA/AML, consumer protection, state insurance regulation and examiner requests all assume a retrievable evidence trail for how a decision was made on a specific date.
Where we start
The workflows that pay first
High volume, high variance, and far enough from the consequential decision that automation is defensible.
Commercial and consumer document intake
Extraction, classification and exception routing on loan files, statements and policy documents — with source citation on every extracted field.
KYC refresh and adverse media review
Agent-assisted screening that drafts the analyst's summary and preserves the underlying evidence, leaving the disposition with the human.
Claims and servicing triage
Coverage summarization and next-best-action for adjusters and servicing agents, with thresholds routing consequential calls to a licensed human.
Control testing and regulatory reporting
Drafting and evidence assembly for first- and second-line testing, reducing cycle time without moving the sign-off.
How the practices apply
Our five practices, read for financial services
- Navigate
- Score candidate workflows against model risk appetite and examiner exposure, then rank by return achievable inside ninety days.
- Deploy
- Forward deployed pods build inside your environment and your change controls — no sandbox that cannot be promoted.
- Amplify
- Place engineers, analysts and PMs who already understand where a model may not sit in a credit or reserving decision.
- Measure
- Put AI spend and performance in the same operating review as everything else the CFO funds.
- Sustain
- Benchmarking, drift alerting and a versioning trail your examiners have accepted before.
Proof
Financial services engagements
Situation, approach, what we built, and the constraint the solution had to satisfy.
Navigate · Financial services
A $140M regional bank funds one AI roadmap instead of eleven pilots
Eleven disconnected AI pilots across five business units became three funded tracks, a governance charter, and a board-approved first investment — in four weeks.
11 → 3
Pilots consolidated into funded tracks
Sustain · Insurance
A specialty insurer stops silent drift in a claims agent six months post-launch
Escalation rates had crept up 14 points while the system answered in the same confident register. Benchmarking, governance and a real feedback loop turned a depreciating asset into a compounding one.
36% → 19%
Escalation rate within two quarters
Roles we deploy
Who shows up
- AI Strategist
- Model Risk Analyst
- Forward Deployed AI Engineer
- AI Governance Analyst
Next step
Book an AI readiness review for financial services
Four weeks, one ranked opportunity register, and a governance structure your risk function can live with.
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