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🛡️ Insurance · Data Flow

Insurance Claim Fraud Scoring

How an insurer scores incoming claims for fraud: data enrichment, rules, an ML model and a special investigation unit queue.

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Drawing diagram…

What this diagram shows

  • Claims are enriched with policy, history and third-party data
  • Rules catch known patterns; ML catches unusual ones
  • Investigator outcomes retrain the model

Prompt used

Data flow for claims fraud scoring: new claims from the claims system are enriched with policy data, the customer's claim history, garage and hospital networks and industry fraud registry hits, then scored by business rules (claim soon after policy start, repeated garages) and an ML model. Low scores go straight to settlement, high scores to the special investigation unit (SIU) queue; investigator findings become labels for model retraining.

Mermaid code
flowchart LR
  CL[New Claims] --> EN[Enrichment]
  POL[(Policy Data)] --> EN
  HIS[(Claim History)] --> EN
  NET[(Garage and Hospital Networks)] --> EN
  REG[(Industry Fraud Registry)] --> EN
  EN --> RU[Rules: early claim, repeat garages]
  EN --> ML[ML Fraud Model]
  RU --> SC[Combined Score]
  ML --> SC
  SC -->|Low| ST[Straight-through Settlement]
  SC -->|High| SIU[Investigation Unit Queue]
  SIU -->|Findings| LB[(Labels)]
  LB -->|Retraining| ML

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