A second set of eyes on every
insurance lead you ingest.
AI-powered insurance lead fraud & anomaly detection. Euclid scores every inbound lead across three risk engines, surfaces the patterns that matter, and routes only what needs a human to your analysts.
Oscar Health
How a lead moves through Euclid
- JOB-7842 carrier_intake_may26.csv layout: auto-detected 47,500 rows Processing · 04:11
- JOB-7841 broker_book_batch_q2.csv scored: 22,000 / 22,000 22,000 rows Completed · 2m ago
- JOB-7840 ffm_extract_q2_2026.csv scored: 38,000 / 38,000 38,000 rows Completed · 4m ago
- JOB-7839 monthly_agent_review.csv queued behind 3 jobs 15,000 rows Pending
Three engines, one Euclid Score
Synthetic identity detection
Checks every lead for fake PII, recycled contact details, and identities that show up under different names across the book. When email and phone APIs return results, those signals feed directly into the score. When they don't, heuristics fill the gap.
Organized fraud patterns
Looks across the whole batch for patterns that only appear when something organized is happening. The same phone on five different names, an agent submitting 300 policies in a week, a cluster of addresses that don't exist. 35 documented rules, derived from real forensic reviews.
Graph · 35 catalogue rules · 6 pattern familiesComorbidity scoring
When health data is present, age, BMI, smoker status, and chronic conditions are scored against a weighted comorbidity matrix. This layer is optional. Carriers without health columns skip it entirely, and the composite re-weights automatically.
Clinical · weighted comorbidity matrixLLM orchestration meets analyst judgement
Theo
Theo handles the parts of the job that don't fit neatly into a rule: reading a carrier CSV format it has never seen before, turning a score into a sentence an analyst can act on, and answering plain-English questions over a scored book. Theo manages prompt routing, PII tokenization, and ties every AI output to a versioned audit record so every flag is traceable.
Euclid-trained models
The scoring models were trained on real broker books with known outcomes, including what clean, legitimate books look like, not just fraudulent ones. A dedicated comorbidity layer adds clinical context on top of identity and fraud signals for carriers whose leads include health data.
Analyst queues
Not everything gets auto-decided. High-risk and borderline leads land in a structured queue where a human makes the final call. Euclid shows the five highest-weight reasons for the flag so analysts spend their time investigating, not re-deriving the score. Overrides feed back into future model improvements.
What powers every Euclid Score
Every Euclid Score can be walked back to a specific rule. The pattern catalogue is derived from three real-world corpora: actual broker-book forensic reviews, carrier-side investigation records, and cross-industry suspension watchlists. Theo processes everything with PII tokenized before any prompt is assembled, and every catalogue version that produced a score is written into the audit trail.
Scoring formula
Hybrid composite. Algorithmic logic enriched by AI pattern recognition and external validation APIs.
- IRC = 0.8·IR + 0.2·FS
- Score = 0.9·IRC + 0.1·HR
- LLM enrichment · weighted by pattern confidence
- Four tiers: Low / Med / High / Critical
Pattern catalogue
Documented fraud signatures derived from real broker-book forensic reviews, not theoretical edge cases.
- Per-policy signatures (subsidy & eligibility)
- Per-broker concentration & velocity
- Per-agency coordination fingerprints
- Every flag links back to a rule
Brokerage oversight layer
Cross-industry signal that elevates risk priors for agents and agencies with recent suspension, termination, or compliance history, independent of any single carrier's view.
- Agency reputation watchlist
- Multi-carrier suspension footprint
- Coaching & compliance history
- Agent-network linkage signals
Versioning & governance
Catalogue versions are immutable. Every score is stamped with the version that produced it.
- PII tokenized pre-prompt (names, emails, phones)
- Score-version audit trail
- Historical scores preserved on update
- Published diff with every catalogue release
Identity fraud
Invalid contact data, suspicious or non-residential addresses, auto-generated email fingerprints, disposable phone numbers.
Inflated households
≥3 lives on majority of policies, ≥6 lives always investigated, FFM application IDs reused across distinct policies.
Premium & subsidy
$0 premium with positive APTC across the book, FPL clustering at subsidy sweet-spots, single-plan-SKU funneling.
Agent red flags
Excessive policy volume per state (250+), cross-state licensing mismatches, future-effective enrollment surges, multi-carrier termination footprint.