Risk · MGAs & Specialty P&C
Insurance Claims Fraud Detection with Machine Learning
Score every claim as it arrives so leakage stops before payout—not after SIU backlog.
Primary search: insurance claims fraud detection machine learning
The problem
Fraud and leakage are found weeks late, if at all. Adjusters treat every FNOL the same. STP never leaves the roadmap.
How we ship it
- 01Score claims at FNOL with explainable risk signals
- 02Route low-risk files to STP candidates
- 03Escalate high-risk to SIU with evidence packs
- 04Measure lift on loss ratio and adjuster hours, not model vanity metrics
Outcome
Stop leakage before settlement. Keep adjusters on the hard files.
KPI: Risk
Related searches this page covers
- insurance claims fraud detection machine learning
- FNOL automation
- straight through processing insurance claims
More MGAs & Specialty P&C workflows
All MGAs & Specialty P&C →Frequently asked questions
Do we have to rip out our policy admin system?
No. We orchestrate against the PAS and the submission inbox you already have. The first mandate is intake, extraction, and scoring, not a core-system replacement.
How is this different from an insurtech platform?
You own the code, the evals, and the models in your cloud. We are not a multi-tenant underwriting workbench. Scoped production in 6–10 weeks, then your team runs it.
How do you scope AI for our industry?
Week-one discovery locks business KPIs—revenue, cost, risk, or cycle time—not model vanity metrics. We map your data sources, compliance constraints, and existing stack, then propose a fixed-scope mandate with acceptance criteria or redirect you to a simpler path.
Do you have experience in our sector?
Proven depth sectors (financial services, federal, real estate, telecom, higher education) reflect production systems we've shipped. Growth and operator verticals (MGAs, credit unions, property management, 3PL, RCM, staffing, licensed iGaming, FFL) are selective mandates where those patterns transfer with industry-specific tuning. Biotech, insurance, and regulated work often runs under NDA.
What do we get at the end of an engagement?
Deployable code in your repos, monitoring and runbooks, documentation your team can operate, and knowledge-transfer sessions. The Production Standard is contracted in writing—no handoff to a junior bench after the sale.
What's the fastest way to start?
Book a discovery call or start with a bounded diagnostic: $2,500 Postgres audit, insurance claims-fraud diagnostic from $7,500, or the free MLOps readiness assessment. Each path surfaces whether AI is the right tool before a larger commitment.
The Production Standard
The Production Standard
Six commitments contracted in writing on every engagement.
01
Deploy or redirect
If AI isn't the right tool, we say so in week one and redirect budget to what will ship. No POC theater.
02
Metrics before models
Business KPIs locked in discovery. Model selection follows the metric, not the hype cycle.
03
Governance from day one
Model cards, access controls, evaluation harnesses, and audit trails, not a compliance bolt-on at go-live.
04
Production artifacts
Deployed code, monitoring dashboards, CI/CD pipelines, and incident runbooks. Not recommendations for someone else to implement.
05
6-week production target
Scoped projects designed for production in 6–10 weeks. Boutiques move; global programs wait for steering committees.
06
Capability transfer
Your team can operate, extend, and maintain what we build. IP and documentation transfer is included, not upsold.
Every scoped project includes a production deliverable checklist signed off before close. If we can't commit to deployable output, we won't take the engagement.
Selective intake · 2026
Your next system shouldship to production.
Agentic development from architecture through deployment, with the Production Standard contracted in writing.