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QGI
Industries · Financial Services

AI for banking, lending, and insurance

QGI Finance combines financial language, mathematics, symbolic constraints, and deterministic computation. Deploy it through QGI Private AI when data, models, agents, and operating controls must remain inside the institution's boundary.

Scope: banking, lending, and insurance only.

Partner on a FinServ demo See the deterministic stack →

7M+

Regulated lending cycles per year (US)

> 20%

Mortgage compliance errors flagged per avg cycle

80%

Target reduction in analysis time (QGI demos)

Provable

Audit trail surface

Banking & lending

Mortgage, credit, AML, and capital markets

  • Mortgage compliance — guidelines, overlays, and loan files with Conflict signals before a decision letter is written
  • Credit underwriting — adverse-action reasons traced to specific rules and data points, replayable for regulator review
  • AML / KYC — typologies and watchlists investigated with traceable chains that survive OFAC or FinCEN inspection
  • Disclosure review — 10-Ks and regulatory filings checked for contradictions before filing, not during an SEC comment letter
Insurance

Underwriting, claims, and fraud triage

  • Underwriting — submission data reasoned against appetite guidelines and treaty language with explainable declines
  • Claims adjudication — policy language, endorsements, and loss reports flagged where exclusions contradict claim context
  • Fraud / SIU triage — investigator-grade narratives constrained to evidence present, replayable at SIU review
Why FinServ needs deterministic AI

Regulators ask one question probabilistic AI cannot answer: show me why.

01

Regulators expect explainable AI

Any AI touching a credit file, claim, or KYC record must be replayable and admissible. Probabilistic retrieval alone does not clear the bar.

02

The cost of being wrong is regulatory

A wrongly-denied mortgage, missed AML flag, or unsupported claim denial invites CFPB scrutiny, class actions, or state AG inquiries.

How the QAG stack maps to FinServ

Three layers. One replayable reasoning chain.

Layer 1 Rules + mathematics

QGI Symbolic

Financial rules, constraints, calculations, and policy conditions remain explicit and reviewable.

Layer 2 Hybrid intelligence

QGI Fusion

Financial language, symbolic constraints, mathematics, domain models, and approved tools work together.

Layer 3 Controlled deployment

QGI Private AI

Models, agents, memory, data, and applications run inside the deployment boundary selected by the institution.

See the full QGI product stack →
Frequently asked

Questions lenders and insurers ask first.

How is QGI's Chunkless RAG different from standard RAG?
Classical RAG chunks documents and retrieves by cosine similarity — polarity and cross-rule dependencies collapse. QGI's Chunkless RAG reasons over whole rule structures via Q-Prime and surfaces seven explicit signals before any output is generated.
What does end-to-end Deterministic AI mean?
Every step from encoding through reasoning to the final decision is structured, logged, and replayable. A regulator can reproduce the reasoning against the rule versions in force on the decision date.
What financial services use cases does QGI cover?
Banking and lending: mortgage compliance, credit underwriting, AML/KYC, disclosure review. Insurance: underwriting, claims adjudication, fraud triage. The common pattern is a regulated decision where the rationale must be signed and defensible.
How is QGI deployed in a regulated enterprise?
Three models: Managed (QGI cloud, fastest for demos), VPC (customer cloud, data stays in tenant), and On-premise (air-gapped). Most demos start managed and graduate to VPC before production.
Partner with QGI

Bring us a regulated workflow.

We work with a small number of regulated teams each quarter. Bring the workflow, rulebook, and audit requirement — we ship the QAG pipeline.

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