Learning Objectives
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# Learning Objectives - Apply link analysis to fraud detection - Identify fraud rings and collusion networks - Use graph algorithms for suspicious pattern discovery - Graph theory basics - Week 7: Data science for fraud ## 1. Link Analysis for Fraud Fraud is rarely isolated - fraudsters are connected to each other,...
Learning Objectives
- Apply link analysis to fraud detection
- Identify fraud rings and collusion networks
- Use graph algorithms for suspicious pattern discovery
- Graph theory basics
- Week 7: Data science for fraud
1. Link Analysis for Fraud
Fraud is rarely isolated - fraudsters are connected to each other, to accounts, to businesses. Link analysis reveals these connections.
Entities: People, accounts, companies, addresses, phone numbers, IP addresses, devices Links: Transactions, shared attributes, communication, relationships
2. Suspicious Patterns
- Circular Transactions: Money flows in a circle (laundering)
- Star Patterns: One entity connected to many (money mule)
- Bipartite Dense: Many entities share same contact info (shell companies)
- Temporal Patterns: Fast transactions before customer profile updates
3. Graph Algorithms
- Community Detection: Louvain algorithm finds fraud rings
- Shortest Path: Find connections between known fraudsters and new entities
- PageRank: Identify important/influential entities in network
- Centrality Measures: Degree (connections), Betweenness (bridges), Closeness (reachability)
- SimRank: Find similar entities based on network structure
Q1: Why is network analysis useful for fraud detection?Fraud rarely involves single entity. Networks reveal relationships between fraudsters, shared contact info, circular money flows, and organizing entities (central figures). Q2: What does a star pattern in a transaction network suggest?One entity receives transactions from many different sources (or sends to many). May indicate a money mule (aggregating funds) or a fraudulent vendor receiving from multiple shell companies. Q3: What is community detection in fraud analysis?Algorithm (Louvain, Infomap) identifies groups of entities with dense internal connections. Each community represents a potential fraud ring or organized crime group. Q4: How does SimRank help detect synthetic identity fraud?Identifies accounts with similar attributes (same device, same email pattern, same address) - likely created by same fraudster using synthetic identities. Q5: What is the difference between degree centrality and betweenness centrality?Degree: number of direct connections (popular entity). Betweenness: how often entity lies on shortest paths between others (gatekeeper/bridge). Betweenness can identify key orchestrators even with few direct connections. Join Discord PreviousData Science for Fraud DetectionNextText Analytics for Forensics