FinTech & Surety · Compliance
Compliance-ready AI for financial decision-making.
Deploying AI in financial decisions attracts regulatory scrutiny. AI underwriting systems must demonstrate explainability, non-discrimination, and auditability. We design for all three from the architecture up — not as an afterthought.
What compliance-ready means
Three things financial AI has to prove.
Deploying AI in financial decision-making attracts regulatory scrutiny. An AI underwriting system has to demonstrate three things: that its decisions can be explained, that they are non-discriminatory, and that they can be audited end to end.
We build those requirements into the architecture rather than retrofitting them. AI assists and flags; human underwriters keep control of material decisions, and every decision leaves a documented trail behind it.
Explainability
Every decision comes with its reasoning.
A score is only useful to a regulator if its reasoning is legible. We attach the why to every output.
SHAP value explanations
Every score carries SHAP value explanations, so the factors that drove it are visible and quantified rather than hidden inside a model.
Full decision log
A decision log captures the full chain — input data to extracted fields to score factors to output — so any decision can be reconstructed exactly.
Plain-language explanation
Plain-language explanation generation turns the math into a sentence an applicant and a regulator can read: for example, declined because working capital declined 34% year over year and the current ratio is below 1.2.
Non-discrimination
Fair lending, built into the model.
Fair-lending obligations under ECOA and FCRA are addressed in how the model is built and monitored.
No protected-class data
Protected-class data is kept out of the scoring models entirely, so decisions are not driven by attributes that fair-lending law prohibits.
Regular fairness testing
Models undergo regular fairness testing across demographic proxies to surface disparate impact that protected-class exclusion alone can miss.
Adverse-action support
FCRA-compliant adverse-action language is generated automatically, supporting the adverse-action notice requirements under ECOA and FCRA.
Audit trail
An immutable record of everything.
Regulatory frameworks
What we design for.
We design financial AI systems against the frameworks that govern AI in lending and insurance.
FCRA
Credit-adjacentFair Credit Reporting Act requirements for credit-adjacent risk scoring, including adverse-action notice support.
ECOA
Fair lendingEqual Credit Opportunity Act fair-lending requirements for underwriting AI, addressed through protected-class exclusion and fairness testing.
FTC Act
ClaimsFTC Act standards for AI marketing and lending claims, keeping representations defensible.
PCI-DSS
Payment dataPCI-DSS aligned handling for fintech systems that touch payment-card data, alongside AES-256 at rest and TLS 1.3 in transit.
State insurance departments
StateState insurance department requirements, which vary by state and shape how AI may be used in underwriting.
NAIC guidance
Insurance AINAIC guidance on the use of AI in insurance, informing governance and documentation expectations.
Audit trail
Immutable, timestamped, regulatory-ready
Explainability, non-discrimination, and auditability designed into the architecture from day one — with human-in-the-loop control on material decisions and SOC2-aligned controls on the audit log itself.