Insurance

AI that underwrites, triages, and detects fraud — at the speed of a click.

Underwriting copilots, claims triage, fraud signal.

Insurance AI lives at the intersection of regulatory complexity and adversarial inputs. Underwriting models need to be fair and explainable. Claims triage needs to be fast without sacrificing accuracy. Fraud detection needs to catch sophisticated rings, not just obvious outliers. We've built AI systems that navigate all three.

Compliance & certifications

NAIC guidelines
FCRA / adverse action
SOC 2 Type II
State DOI requirements
GDPR (EU lines)
TCPA (outreach)

Why Evolve Edge

Actuarial depth Our team understands loss ratios, combined ratios, and the difference between frequency and severity models. We speak insurance, not just AI.

Fair lending and FCRA alignment Model fairness analysis, disparate impact testing, and adverse action reason generation built into every underwriting model before it touches a real applicant.

Fraud pattern expertise We've trained models on staged accident schemes, provider fraud networks, and application fraud rings. Network-level pattern recognition, not single-claim inspection.

Evolve Edge team at work

Use cases

Automated underwriting

AI-augmented risk scoring that processes applications in seconds — with rules-based overrides and human review queues for edge cases and flagged risks.

Claims triage and routing

Automated severity scoring, documentation completeness checks, and intelligent routing to the right adjuster — cutting days off average cycle time.

Fraud detection

Network analysis, anomaly detection, and behavioral signals that catch fraud rings missed by single-claim inspection and rule-based systems.

FNOL voice automation

Voice agents that handle first notice of loss intake — capturing all required fields and automatically escalating injury claims for human review.

Renewal copilots

Agents that handle renewal outreach, cross-sell recommendations, and quote generation for P&C and life lines — reducing lapse rates.

Document processing

Medical record extraction, repair estimate parsing, and policy document analysis fed into adjudication workflows with structured output.

Client perspective

Claims cycle time dropped 31% in the first quarter. The fraud detection is catching patterns our manual review team was completely missing.

Lisa TranHead of Claims Technology · Apex Insurance Group

FAQ

How do you ensure underwriting models are fair?
Pre-deployment fairness audits using disparate impact testing across protected classes, ongoing monitoring for demographic drift, and documented adverse action reason codes.
Can you handle complex claims documents like medical records?
Yes. We've built OCR + extraction pipelines that parse medical records, HCFA forms, repair estimates, and police reports — with human review queues for ambiguous extractions.
How do you detect fraud rings, not just individual claims?
Graph-based network analysis that connects entities (providers, claimants, attorneys, repair shops) and surfaces clusters of suspicious relationships invisible to single-claim inspection.

Have Questions? Let's Talk.

Free 30 minute call with a senior engineer, not a salesperson. We have got the answers to your questions.