Industries · Financial Services

AI for banking, lending, and insurance

QGI ships deterministic AI for regulated financial decisions — every compliance officer, regulator, and auditor can replay the full reasoning chain. Chunkless RAG via the QAG Engine surfaces contradictions in policy and regulatory corpora before the decision is made.

Scope: banking, lending, and insurance only.

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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.

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.

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 Encoding

Q-Prime

Rulebooks, guidelines, and policy documents encoded as a quantum-structured hypergraph — polarity, scope, and exceptions stay attached.

Layer 2 Reasoning

QAG Engine

Seven HSC signals (Relevance, Conflict, Coverage, and more) surface contradictions before generation — readable by compliance officers.

Layer 3 Execution

Neural Symbolic Agents

Multi-step workflows run as logged, replayable agents. The audit trail is the runtime, not a PDF generated at the end.

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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Partner with QGI