Question 1

Do you work with general, life, health, or specialty insurers?

Accepted Answer

All of the above. The underlying pipeline (intake → validate → assess → fraud → settle) is LOB-aware rather than LOB-locked — each line has its own policy-admin integrations, peril models, and fraud features, but they run on shared infrastructure. Specialty lines (marine, aviation, parametric, cyber) typically need bespoke peril models, but the claims pipeline itself stays consistent.

Question 2

Can you integrate with our policy admin and claims systems?

Accepted Answer

Yes. Common integrations include Guidewire ClaimCenter and PolicyCenter, Duck Creek, Majesco, and Sapiens on the modern side, and a long tail of mainframe and legacy cores via adapter layers (SOAP, fixed-width files, MQ, bespoke APIs). For proprietary cores, we build integration layers with idempotency, retries, and reconciliation baked in — no fragile script-and-pray arrangements.

Question 3

How is your fraud model different from rule-based systems?

Accepted Answer

Rule-based systems catch the patterns you already know about, and generate painful false-positive rates as fraudsters adapt. Our approach pairs network-graph analytics (who-knows-who, who-repairs-for-whom, who-references-whom) with similarity search against your entire historical claims database — so novel fraud patterns that share DNA with past cases still surface. Rules stay in the stack for known-bad patterns; ML catches the rest. False-positive rates typically drop 40-60% at equal true-positive rate.

Question 4

How do you handle jurisdictional differences (IFRS 17, local regulators)?

Accepted Answer

Our actuarial and reporting layers are policy-driven — IFRS 17 measurement models (PAA, GMM, VFA) are supported natively, with each jurisdiction's local regulator expectations (SAM, IAIS ICS, local SII equivalents) modelled as additional disclosure layers on top. That means a group insurer operating across multiple jurisdictions runs one pipeline, not five copies, and local regulator updates are a mapping change, not a re-architecture.

Question 5

What's the typical straight-through rate on claims?

Accepted Answer

For motor claims with photo evidence, mature deployments typically hit 80-90% straight-through on low-severity claims (damage under a configured threshold, clean fraud signals, valid coverage). For property, it's usually 50-70% — more variation in claim complexity. For life and health, STP rates depend heavily on product structure, but the automation value is often in triage and reserve accuracy rather than full auto-settlement.

Question 6

How do you handle edge cases and appeals?

Accepted Answer

Every auto-decision generates a reasoning trail — which features drove the decision, which thresholds were triggered, which override rules applied. For appeals or disputes, that trail is the starting point: the reviewing adjuster sees exactly why the auto-engine decided what it did, and can override with a documented reason. Overrides feed back into the retraining cycle, so the system learns from the edge cases over time.

Question 7

How do we measure ROI — is it loss-ratio impact?

Accepted Answer

Primary ROI signals vary by use case. For claims automation, it's cycle time reduction and claims-handling cost savings, often 30-50% on operational cost. For fraud, it's fraud-save (claims denied or reduced that would previously have paid out) — typically 2-6% of claims spend depending on line of business. For underwriting analytics, it's loss-ratio improvement on new business, usually 2-5 percentage points within 18 months. InsightAX reports each of these separately, so you can see what's working.

Insurance AI: faster claims, smarter underwriting, fewer fraud leaks.

Every layer of the insurance chain — quote to claim to SIU.

Claims Automation

Underwriting Analytics

Claims-Fraud Detection

Agent Productivity

Actuarially sound. Fraud-aware. Loss-ratio-tied.

The practices that power every insurance deployment.

Actuarially-sound,
audit-ready by default.

One claims line or a full stack —
priced to the deployment.

Common questions.

Your claims chain, engineered.