Build no. 06
AI-Driven Embedded Finance & Third-Party Oversight
BaaS & Fintech Partnership RiskCommunity Bank or Credit Union · BaaS / fintech programsConfigured
The technical solution — and the human capability to operate it responsibly.
- Adopter assets
- ~$1B
- Customers served
- 25,000
- Evidence outcomes
- 5
- Adopters / vendors
- 1 / 1
First Federal Bank of Kansas City, Q1 2026
Across 9 Kansas City-metro branches
Risk, use case, vendor, failure, monitoring — the examiner checklist
FFBKC × ARMAI — early-mover recursive-governance pattern
The build, in two halves
Part I
The Technical Build
What the model, retrieval, deployment, and controls must actually do.
Model & Retrieval
Deployment
Controls
Channels
Part II
The Human Build
The roles and skills that keep the deployment defensible under exam.
AI Compliance Officer
Keeps the oversight program — and the tool itself — inside the regulatory perimeter.
What makes it defensible
IT Lead for AI Integration
Connects the review platform to vendor-management and core systems.
What makes it defensible
AI Governance Committee Member
Owns the recursive-governance question: who governs the tool that governs your vendors?
What makes it defensible
Part III
Verification Benchmarks
Proposed controls in Bank Ready AI’s reference design — not tests or thresholds reported by the adopting institution.
- Five evidence outcomes produced per partner review: risk analysis, use-case documentation, vendor governance, failure response, performance monitoring
- Parallel-run parity: AI due-diligence file matches or exceeds manual review completeness
- Audit-trail traceability: every risk determination resolves to named source evidence
- Recursive-governance check: the tool's own TPRM file and validation stay current
The visible skills overlap across the adoption roles. It is the revealed governance depth that differentiates what each role actually secures — and that turns a technical deployment into a defensible one.
Proven in the field
1 confirmed adopter
Institutions confirmed running this use case in our production research database — this build reflects how it is deployed in practice.
- First Federal Bank of Kansas City
Make it yours
Where institutions customize
A reference build is a starting point, not a prescription. These are the decisions that make it yours.
- BaaS program scope: which fintech partners onboard to AI oversight first
- Due-diligence depth and refresh cadence by partner risk tier
- Mapping the five evidence outcomes to your examiner's checklist
- Integration path into your core and vendor-management stack
- Escalation authority when the AI flags a partner finding
The integration plan
Every phase moves the technical build and the human build together — the deployment and the capability to govern it arrive at the same time.
Phase 1 · Foundation
Govern the tool before it governs anyone
Technical workstream
- Vendor due diligence on the oversight platform itself — the recursive step comes first
- Ringfence input sources: which policies, contracts, and SOC reports the engine may ingest
Human workstream
- Stand up the TPRM file and model-inventory entry for the AI tool itself
- Committee approves scope, risk appetite, and the five evidence outcomes as the acceptance bar
Phase 2 · Pilot
One partner, parallel-run against manual review
Technical workstream
- Run AI due diligence on a single fintech partner alongside the existing manual process
- Benchmark AI evidence outputs against the manual file for completeness and accuracy
Human workstream
- Risk staff disposition AI findings with documented rationale
- Committee reviews parity results and the audit-trail quality before expansion
Phase 3 · Production
Full portfolio on the evidence-outcome cadence
Technical workstream
- Expand to the full partner portfolio with monitoring dashboards
- Automate evidence-outcome documentation for examination requests
Human workstream
- Quarterly board reporting on program risk posture
- Annual revalidation of the oversight tool — recursive governance as a standing agenda item
Human-workstream roles and skills are drawn from the Talent Builder — the same library used to generate role profiles at /create/talent.
20-minute go/no-go call
Identify the next step for your AI specification.
A complimentary 20-minute go/no-go call with Alex Zaboli to discuss one AI use case and make a preliminary decision on the next step: explore further, gather more information, or pause. This is not a conclusive feasibility assessment; technical, operational, and governance diligence is required before an implementation decision.
Microsoft Bookings lets you book as a guest — no Microsoft account is required.