Computer Science – 18.1 Artificial Intelligence (AI) | e-Consult

To identify fraudulent transactions, a graph data structure is highly effective. The financial network can be represented as a graph where:

• Nodes: Represent entities such as:

  • Accounts (e.g., customer accounts, merchant accounts)
  • Transactions (individual financial transfers)
  • Devices (used for transactions)
  • IP Addresses (used for transactions)

• Edges: Represent relationships between these entities, such as:

  • "Transferred to" (between accounts)
  • "Initiated by" (between accounts and devices)
  • "Made from" (between accounts and IP addresses)

Graph Algorithms: Several graph algorithms can be applied:

• Community Detection: Algorithms like Louvain Modularity can identify groups of accounts that frequently transact with each other. Unusual or suspicious communities might indicate fraudulent activity.
• Centrality Measures: Algorithms like Betweenness Centrality can identify accounts that act as bridges between different parts of the network. Accounts with high betweenness centrality might be involved in fraudulent schemes.
• Pathfinding and Anomaly Detection: Analyzing paths between accounts can reveal unusual transaction patterns. For example, a series of transactions between a legitimate account and a known fraudulent account could be flagged. Graph algorithms can be used to detect unusual path lengths or sequences of transactions.
• Graph Embedding: Techniques like Node2Vec or GraphSAGE can create vector representations of the nodes in the graph. These embeddings can then be used with machine learning models to predict the likelihood of a node being involved in fraudulent activity.

Advantages:

• Detecting Complex Schemes: Graphs can reveal complex, interconnected fraud schemes that might be missed by traditional rule-based systems.
• Adaptability: The graph model can be easily updated to incorporate new data and identify emerging fraud patterns.
• Explainability: The graph structure provides a visual representation of the relationships involved in a potential fraud, making it easier to understand and investigate.

Disadvantages:

• Computational Cost: Analyzing large graphs can be computationally expensive, requiring efficient algorithms and infrastructure.
• Data Quality: The accuracy of the fraud detection system depends on the quality and completeness of the graph data. Inaccurate or missing data can lead to false positives or false negatives.
• Privacy Concerns: Representing financial transactions as a graph raises privacy concerns that need to be addressed through appropriate anonymization and security measures.