AI for the firms that move money.
For finance firms that need one board-ready view of where AI is creating value, where risk is building, and what to do next.
Built for regulated finance teams that need adoption, evaluation, and evidence to survive procurement, model risk, and board scrutiny.
The AI Audit gives finance firms one operating view: use, value, risk, and the workstream to sequence next.
Serve the finance buyer map.
| Sub-sector | Buyer titles |
|---|---|
| Banks (community + regional) | CIO, Chief Risk Officer, Head of Innovation |
| Credit unions | CEO, CIO |
| Asset / wealth managers | COO, Head of Technology |
| Capital-markets infrastructure | CTO, Head of Engineering |
| Payments / lending fintech | CTO, VP Engineering, CISO |
| Banking-ops software (PE-backed) | CIO (post-M&A integration shape) |
Three sub-sector pages drill in.
Banks and credit unions; fintech (payments, lending, banking-ops, capital-markets tech, treasury); asset and wealth and capital markets. Same regulatory perimeter, different buyer-specific workflows.
- Banks & Credit Unions. /industries/banks → SR 11-7 anchored for community banks, regional banks, and credit unions.
- Fintech. /industries/fintech → payments, lending, banking-ops, capital-markets tech, treasury and CFO software.
- Asset & Wealth + Capital Markets. /industries/asset-wealth → asset managers, wealth managers, broker-dealers, capital-markets infrastructure.
Transform concrete workflows.
Where AI Transformation lands inside regulated finance. One workflow at a time, instrumented end to end.
- KYC and onboarding. document ingestion, automated risk scoring, exception triage.
- AML and transaction surveillance. agent-driven alert triage, false-positive reduction.
- Trade surveillance. pattern detection, regulatory query response.
- Customer support. chatbot deflection with strict policy boundaries.
- Vendor risk and AI vendor compliance. continuous monitoring across third-party model behavior.
- Internal AI tooling rollout. vibe-coding governance, enterprise AI chatbot, search.
One pipeline. Three classes of evidence.
The AI Audit produces the operating read. AI Governance routes that evidence into the framework, regulation, and guideline layers your CRO and Compliance team already operate against.
- Standards and frameworks. ISO 42001, NIST AI RMF, AIUC-1.
- Regulations. SR 11-7 (model risk, US banking), GDPR, CCPA, NYDFS Part 500, FFIEC, EU AI Act.
- Guidelines. industry codes of practice and supervisory expectations.
Same evidence pipeline produces the operational view and the audit-grade trail.
Anchored on the AI Audit.
Two-week visibility deliverable, then the three workstreams sequence per your priority. One named TrustEvals practitioner embeds; methodology transfers, the platform stays.
Start with Audit. Sequence the workstreams.
One order, applied across the engagement. The AI Audit produces the operating read, then AI Transformation, AI Governance, and AI Fluency sequence per the customer's priority.
- AI AuditSee use, value, and risk.
- AI TransformationShip value workflows.
- AI GovernanceProduce audit evidence.
- AI FluencyRaise role-level capability.
Start with an AI Audit baseline.
Discovery call. Calendar link within 60 seconds.
Frequently asked.
Yes. We serve community banks, regional banks, and credit unions. The Adoption & Efficiency Gain Report Template was built for this band of institution.
Different layer. Existing tooling covers deterministic and statistical models. We add the LLM and agent evaluation layer alongside, with framework-mapped evidence that survives the next exam.
Our evidence pipeline produces the artifacts your model risk team needs. We don't run the review; we feed the auditor.