Portfolio Accelerator · Insurance · Real-Time Fraud Detection
Insurance AI Governance Consulting: Fraud Detection Built on a Real, Auditable Accelerator
A working fraud-detection and anomaly-scoring accelerator, governed for insurance compliance review — not a slide deck.
problem
Why Insurance Fraud Detection AI Needs a Governance Layer, Not Just a Model
A fraud-scoring model that flags claims without an audit trail is a liability, not a control. Insurers evaluating AI-driven fraud detection are increasingly asked by examiners and reinsurers to show how a model reached a decision, not just that it flagged one — NAIC's model governance guidance and the broader shift toward SR 26-2-style model risk review both assume the model is explainable and its decisions are logged.
Most fraud-detection vendors sell a black-box score. Most AI governance consultancies sell a framework document. Insurers evaluating both categories are left assembling the connective tissue themselves — the governed, explainable, audit-ready fraud-scoring pipeline — on their own.
demo
The Accelerator: A Working Fraud Detection Proof-of-Concept, Not a Slide Deck
This page showcases a working accelerator from Kriv AI's portfolio of demo-built systems, run on real cloud infrastructure against synthetic transaction data (no client data, no PHI/PII).
What the Demo Actually Does
The accelerator scores each incoming transaction for fraud risk, attaches a feature-level explanation for the score (not a bare number), applies a compliance overlay rule set, and routes high-risk items to a human-review queue — publishing every scored event to an append-only audit log. The pipeline runs end-to-end on deployed cloud infrastructure, not a local notebook.
What It Proves — Metrics and Cloud Footprint
In our synthetic benchmark testing, the scoring pipeline returns a risk decision with feature-level explanation in well under half a second per transaction, with every scored event logged to a durable audit trail that can be reconstructed end to end for a reviewer. That combination — sub-second scoring, explainability attached to every decision, and a complete audit trail — is the specific proof point insurers should ask any fraud-AI vendor or consultant to demonstrate live, not describe in a deck.
differentiation
How This Differs From a Big 4 Insurance AI Governance Engagement
Big 4 and SI advisory pages in this space are long on frameworks, case-study logos, and capability statements — and short on anything a prospect can see running. Point-solution fraud vendors show product screenshots and claimed detection rates but are typically silent on model risk management, audit trails, or NAIC-style model governance language.
Kriv AI's structural edge: a named, inspectable accelerator with real cloud infrastructure and an explainability-and-audit architecture, fused with governance depth most point-solution vendors don't carry — in one page, backed by a system you can ask to see live.
governance
Governance Controls Built Into the Fraud Detection Pipeline
Model Risk, Bias Testing, and Explainability for Fraud Scoring
Every scored transaction carries a feature-attribution explanation, not just a risk number — the foundation for the bias testing and model-risk review insurers need to satisfy examiners under evolving model risk management guidance (including the transition from SR 11-7 to SR 26-2 for institutions that map their AI governance to Fed model-risk frameworks).
Audit Trails, Human-in-the-Loop, and Regulatory Reporting
High-risk determinations route to a human reviewer before any downstream action, and every scoring decision — input, output, explanation, and reviewer disposition — is written to an audit log designed to be reconstructable for a regulator or internal auditor on request.
engagement
Engagement Model: From Accelerator Demo to Production Fraud Detection
The accelerator is a starting point, not the deliverable: a scoped engagement adapts the same governed-scoring architecture to your claims data model, your policy rules, and your existing case-management workflow, with the governance and audit-trail layer carried through from day one rather than bolted on after a pilot.
audience
Built for Claims, SIU, and Compliance Teams
The review queue and audit trail are designed around how Special Investigations Units and claims compliance teams actually work: a prioritized, explainable queue for investigators, and a reconstructable decision trail for compliance and audit.
Straight answers
Frequently asked questions about Insurance AI Governance Consulting: Fraud Detection Built on a Real, Auditable Accelerator
Is the fraud detection accelerator tested on real insurance claims data?
No — the accelerator runs entirely on synthetic transaction data. No client data, PHI, or PII is used in the portfolio demo. Production engagements are scoped separately against your real data under your own security and compliance controls.
Does the accelerator do graph-based fraud ring analysis?
The portfolio accelerator shown here focuses on real-time, per-transaction anomaly scoring with explainability and audit logging. Fraud ring / network-level analysis is a related but separate capability we scope on a project basis, not something this specific demo includes today.
What makes this different from a typical fraud-detection vendor tool?
Most fraud-scoring products return a bare risk number. This accelerator pairs the score with a feature-level explanation and a full audit trail for every decision — the governance layer insurers increasingly need to satisfy model risk reviews, not just a detection rate.
How does this map to NAIC AI model governance guidance?
NAIC's model governance expectations center on explainability, documented testing, and audit trails for models used in underwriting or claims decisions. The accelerator's explainability-per-decision and append-only audit log are built directly against that expectation.
How long does it take to go from this demo to a production pilot?
Engagement timelines depend on your data access and integration scope. A typical path starts with a scoped discovery phase against your claims data model, followed by a governed pilot before any production rollout — see our pricing page for engagement structure.
Can we see the accelerator run live before signing a contract?
Yes — we can walk through the live accelerator against synthetic data in a discovery call. Contact us to schedule a working session.
Ready to see the accelerator run against your data model?
Bring your requirements to a working session and we'll walk through the live system.
Book a Discovery Call