What Makes an Effective Fraud Detection Model?

A good model doesn't rely on a single variable. It cross-references dozens of signals: client history, declaration consistency, claim timing, cross-referencing with public databases.

The Signals Analyzed

  • Claim frequency vs. sector average
  • Gap between event date and filing date
  • Inconsistency between submitted photos and description
  • Multiple filings in a short time window
  • Semantic analysis of descriptions (keywords, phrasing)

Real-Time Risk Scoring

Each file receives a score from 0 to 100. Below 20: standard automatic processing. Between 20 and 60: expert review. Above 60: deep investigation.

This scoring doesn't replace the expert — it makes them more efficient. The expert spends less time on legitimate files and more on suspicious cases.

What You Need to Know

AI fraud detection isn't infallible. It has a false positive rate. That's why humans stay in the loop for edge cases. AI is a tool, not a judge.