Fraud Detection and Forensic Intelligence: The AI Detective

In the world of fraud, criminals don't leave "Smoking Guns" in a single spreadsheet. They hide their tracks through a Network of Shadows. They use multiple bank accounts, shared mailing addresses, and "Straw Man" directors. Traditional fraud detection uses "If/Then" rules, but Graph RAG uses "Connection Reasoners."

In this lesson, we will look at how to build a Forensic Graph. We will learn how to identify Circular Money Flows and Shared Identities. We will see how an AI investigator can ask: "Is there any suspicious connection between Account A and the offshore company B?" and get a detailed report of the 5-hop path that links them.

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1. The Fraud Graph Schema

• (:Account) {balance, risk_score}
• (:Person) {SSN_hash, birth_date}
• (:Address) {street, zip}
• (:Digital_Identity) {IP_address, Device_ID}

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2. The "Shared Point" Revelation

Fraudsters often slip up by sharing a single piece of infrastructure.

• Two "Unrelated" accounts use the same Phone Number.
• They both log in from the same IP Address within 3 seconds of each other.

A standard database sees two separate rows. A Graph DB sees two nodes pointing to the same Digital_Identity node. The RAG system can then alert the investigator: "Warning: These two accounts are acting as a single entity."

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3. Circular Transfers (Laundering)

A classic laundering pattern: A -> B -> C -> A. The money is moved through multiple nodes to obscure the source.

The Graph Solution: The Shortest Path algorithm (Module 9) is used specifically to look for cycles. If an AI agent detects a cycle in the transaction log, it classifies the context as "High Risk" and summarizes the flow of funds for the investigator.

graph LR
    A[Account 1] -->|Transfer| B[Middleman 1]
    B -->|Transfer| C[Middleman 2]
    C -->|Transfer| D[Offshore Shell]
    D -->|Transfer| A
    
    subgraph "Money Laundering Cycle"
    A --- B --- C --- D --- A
    end
    
    style A fill:#f44336,color:#fff
    style D fill:#f44336,color:#fff

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4. Implementation: Finding Shared Infrastructure in Cypher

MATCH (a1:Account)-[:LOGGED_IN_FROM]->(ip:IPAddress)<-[:LOGGED_IN_FROM]-(a2:Account)
WHERE a1.id <> a2.id
MATCH (a1)-[:HAS_PHONE]->(p:Phone)<-[:HAS_PHONE]-(a2)
RETURN a1.id, a2.id, ip.address, p.number;

// This query finds two accounts that share BOTH an IP and 
// a phone number—a 99% indicator of a shared user or 'Bot Farm'.

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5. Summary and Exercises

Forensic Graph RAG provides the "Red String" on the investigation board.

• Shared Attributes (IP, Phone) reveal hidden identities.
• Cycle Detection identifies money laundering patterns.
• Path Navigation allows investigators to trace funds across multiple countries/banks.
• Risk Scoring (Module 11) helps prioritize which "Investigation" the AI should work on next.

Exercises

1. Investigative Logic: You are investigating "Insurance Fraud." List 3 node types you would want to connect to an (Accident) node. (e.g., Doctor, Lawyer, Workshop).
2. The "Vague" Connection: A user asks: "How are these two people related?". The graph shows they lived in the same apartment building 5 years apart. Is this "Fraud"? (Hint: It's a weak signal; how would you weigh this in an Evidence Score?).
3. Visualization: Draw a "Star" graph where 10 accounts are all connected to 1 "Digital Identity" node. What does this represent to a fraud investigator?

In the next lesson, we will look at a more creative use case: Recommendation Engines with LLM Reasoning.