Hesper AI
Data & Systems

Network Analysis (Fraud Detection)

Network analysis in fraud detection is the technique of mapping and analyzing relationships between entities involved in insurance claims - claimants, providers, attorneys, witnesses, and addresses - to identify hidden connections that suggest coordinated fraud activity.

In this article

How network analysis worksWhat network patterns revealAI-powered network analysisKey pointsHow Hesper AI helpsFAQ

How network analysis works

Network analysis builds a graph of connections between claim entities. Nodes represent people, businesses, addresses, phone numbers, and vehicles. Edges represent relationships: shared addresses, co-involvement in claims, patient-provider relationships, attorney-client connections. By visualizing and analyzing this graph, investigators can identify clusters of connected entities that appear across multiple seemingly unrelated claims - the signature of organized fraud.

What network patterns reveal

Network analysis uncovers: fraud rings (tightly connected groups filing coordinated claims), suspicious providers (medical practices or body shops connected to an unusual number of claims), staged accident patterns (the same vehicles or individuals appearing in multiple collision claims), and organized crime networks (complex structures spanning multiple states and claim types). These patterns are nearly impossible to detect by examining individual claims in isolation.

AI-powered network analysis

Traditional network analysis required manual graph construction by skilled analysts. AI has transformed this by automatically building and analyzing relationship graphs across millions of claims in real time. Machine learning algorithms identify suspicious cluster patterns, calculate risk scores for network membership, and flag connections that warrant investigation. This scales network analysis from a handful of manually-identified cases to continuous monitoring of the entire claims book.

Key points

  • Maps relationships between claimants, providers, attorneys, witnesses, and addresses
  • Reveals fraud rings, suspicious providers, and coordinated schemes
  • Patterns are invisible when examining individual claims in isolation
  • AI automates graph construction and pattern detection across millions of claims
  • Most effective technique for detecting organized fraud activity
How Hesper AI helps

Hesper AI performs automated network analysis during every investigation. The AI agent maps all connections between the claimant, providers, attorneys, and other involved parties - cross-referencing against the carrier's full claims history to surface hidden links that indicate coordinated fraud.

Related glossary terms

Fraud RingISO ClaimSearchStaged Accident

Frequently asked questions

Network analysis requires claims data with entity identifiers: names, addresses, phone numbers, SSN/tax IDs, vehicle VINs, provider NPIs, and attorney bar numbers. The more data points available, the more connections can be mapped. Cross-carrier data (from ISO ClaimSearch or NICB) dramatically increases effectiveness by revealing connections that span multiple insurers.

AI network analysis is highly effective at identifying suspicious patterns, but not every flagged connection indicates fraud. False positive rates depend on the model and data quality, typically ranging from 10-30%. The key advantage is recall - AI catches patterns that human analysts would miss entirely. Best practice is to use network analysis as a prioritization tool, with human investigators verifying flagged patterns.

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