KYC and AML review
Assembling and summarizing evidence for alert dispositioning, with every assertion traced to source. Analyst throughput improves; the decision stays human.
AI for Financial Services
Model risk, explainability, and controls that survive examination — not a pilot that stalls at second line review.
The constraint
The second line is the real gate
In most sectors the blocker is the security team. In financial services it is model risk management, and they ask harder questions.
Explainability is mandatory, not desirable. A model whose output cannot be explained, reproduced, and challenged will not clear second line review — regardless of how well it performs. That shapes the architecture toward retrieval with citations and away from unexplained end-to-end generation.
Examiners will ask. Anything touching credit decisions, customer treatment, or regulatory reporting attracts scrutiny, and "the vendor’s model produced it" is not an answer. You need documented lineage, versioning, and evidence of ongoing monitoring.
The institutions moving fastest are the ones that engaged model risk in week one rather than presenting to them at the end.
Applications
Where AI creates value in a financial institution
Credit memo drafting
Producing first drafts from financial statements, covenant documents, and prior memos — with citations that let the credit officer verify rather than trust.
Client reporting
Generating commentary from portfolio data under templates and tone controls that keep it consistent and reviewable.
Complaint triage
Classifying and routing complaints, surfacing regulatory-reportable patterns that manual sampling misses.
Research synthesis
Querying across internal research, filings, and market data without material leaving the institution’s boundary.
Compliance
Regulatory landscape
| Framework | What it drives in the architecture |
|---|---|
| SR 11-7 | Model risk management guidance. Drives documentation, validation, and ongoing monitoring requirements — and is where most generative AI programmes stall. |
| GLBA | Governs customer financial information. Private deployment removes the third-party processor question rather than contracting around it. |
| FINRA / SEC | Communications supervision and recordkeeping obligations attach to AI-generated client-facing content. |
| SOX | Where AI touches financial reporting, control design and evidence apply as they would to any system in scope. |
| DORA | For EU-facing operations, adds operational resilience and third-party risk obligations that favour in-house deployment. |
We align AI governance to NIST AI RMF and ISO/IEC 42001 alongside your sector-specific obligations.
Integration
Integration with financial systems
We integrate with core banking platforms, Salesforce Financial Services Cloud, market data providers including Bloomberg, document management, and the enterprise data warehouse. Entitlements are enforced at query time against your existing directory, so information barriers and need-to-know restrictions are respected by the retrieval layer rather than assumed.
Questions
Frequently asked
How do we satisfy model risk management for a generative system?
Treat it as a system, not a model. Document the retrieval and generation pipeline, version prompts and models as controlled artefacts, evaluate against a maintained test set with agreed thresholds, and monitor for drift in production. We produce that documentation as an engagement deliverable.
Can output be reproduced for an examiner?
With logged inputs, retrieved context, model version, and parameters, you can reconstruct what the system saw and why. Non-determinism means the wording may vary; the evidence base and decision path do not.
Is on-premise necessary, or is our cloud tenant enough?
For many institutions a private cloud deployment inside your existing tenant and controls is sufficient and much faster to stand up. We model both. On-premise becomes compelling at sustained volume or where cloud is restricted outright.
Related
Private AI
Production AI systems running entirely inside your infrastructure, with no third-party processor in the data path.
Private AIDeployment Models
Managed API, private cloud, on-premise, edge, and air-gapped compared — including where each one fails.
Compare modelsDiscuss your constraints.
The assessment evaluates your data, infrastructure, and regulatory position, then recommends a deployment architecture your review process can actually approve.
We will sign your NDA before a detailed technical discussion.