Finance

Multi-Agent Fraud Investigation

Overview

What It Is

Agent teams that conduct comprehensive fraud investigations including evidence gathering, pattern analysis, case building, and regulatory reporting.

Agent Types
Case Intake AgentEvidence Collection AgentPattern Analysis AgentNetwork Analysis AgentTimeline Reconstruction AgentWitness/Statement AgentReport Generation AgentRegulatory Filing Agent
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Deep Dive

Overview

Multi-agent fraud investigation systems go beyond detection to conduct comprehensive investigations. Agent teams gather evidence, analyze patterns, map criminal networks, and build cases—transforming fraud response from reactive cleanup to proactive investigation and prevention.

Architecture

Fraud Alert → Case Intake Agent → Investigation Case
                     ↓
          Evidence Collection Agent → Evidence Repository
                     ↓
           Pattern Analysis Agent → Fraud Patterns
                     ↓
           Network Analysis Agent → Related Entities
                     ↓
      Timeline Reconstruction Agent → Event Timeline
                     ↓
          Report Generation Agent → Investigation Report
                     ↓
          Regulatory Filing Agent → SAR/Compliance Filings

Agent Roles

Case Intake Agent

  • Receives fraud alerts from detection systems
  • Assesses initial severity and scope
  • Assigns investigation priority
  • Creates case file and assigns resources

Evidence Collection Agent

  • Gathers transaction records and logs
  • Collects communication records
  • Retrieves account histories
  • Preserves evidence chain of custody

Pattern Analysis Agent

  • Identifies fraud scheme patterns
  • Compares with known fraud typologies
  • Detects anomalies in behavior
  • Quantifies fraud exposure

Network Analysis Agent

  • Maps relationships between entities
  • Identifies connected accounts
  • Discovers shell companies and nominees
  • Visualizes criminal networks

Timeline Reconstruction Agent

  • Builds chronological event sequence
  • Identifies key decision points
  • Correlates actions across systems
  • Highlights suspicious timing

Witness/Statement Agent

  • Analyzes customer statements
  • Identifies inconsistencies
  • Prepares interview questions
  • Summarizes testimonial evidence

Report Generation Agent

  • Compiles investigation findings
  • Writes narrative summaries
  • Prepares evidence exhibits
  • Formats for legal proceedings

Regulatory Filing Agent

  • Prepares Suspicious Activity Reports (SARs)
  • Ensures regulatory compliance
  • Meets filing deadlines
  • Tracks regulatory responses

Investigation Types

Account Takeover

1. Identify compromised credentials
2. Trace unauthorized access
3. Map fraudulent transactions
4. Identify money movement paths
5. Assess customer impact

Money Laundering

1. Trace fund sources
2. Identify layering techniques
3. Map shell company networks
4. Document integration methods
5. Prepare regulatory filings

Insurance Fraud

1. Analyze claim patterns
2. Identify staged incidents
3. Verify claimant histories
4. Document inconsistencies
5. Build prosecution case

Real-World Results

Financial Institutions:

  • 60% faster investigation completion
  • 40% more fraud recovered
  • 3x more cases handled per investigator
  • Better regulatory examination outcomes

Key Patterns

  • Tool Use Pattern: Integration with transaction systems, public records, analytics
  • Reflection Pattern: Learn from closed cases to improve future investigations
  • Human-in-the-Loop: Investigators validate findings and make judgment calls
  • Audit Trail Pattern: Complete documentation for legal proceedings
Evaluation Challenges

Investigation quality is measured by case outcomes (recovery, prosecution). False leads waste investigator time. Evidence quality matters more than quantity. Regulatory acceptance of AI-gathered evidence varies.

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