Example applications of Graph Network assessment algorithms in tackling money-laundering


Pagerank, Closeness, Degree, etc.

Example in AML: Closeness scores detect central players, liaisons (betweenness), most relevant (Page Rank) in a path between customer and high-risk endpoint.

Community Detection

Louvain Modularity, Label Propagation, Strongly and Weakly Connected Components, etc.

Example in AML: Label Propagation to detect common entities and strongly connected components high-risk rings. These are all based on relationships in the graph

Link Prediction

Common Neighbors, Preferential Attachment, Adamic Adar, etc.

Example in AML: Link prediction algorithms based on provenance to identify hidden COLLABORATOR relationships. For example, in AML, these new relationships can, in turn, further inform the analysis of small deposit accounts that involve layering, velocity, concentration, etc.


Jaccard, Cosine, Overlap, etc.

Example in AML: Used for entity resolution. Also, if there is a path between a customer and a high risk end point, similarity algorithms can indicate how similar each path is to other paths from that specific customer to those high risk end points. A company fighting money laundering could then create a subgraph of paths (e.g. paths A, B, C, and have weighted relationships representing similarities among the paths)

Path Finding & Search

Breadth First Search, All Pair Shortest Path, etc.

Example in AML: Identify payment chains and third parties layered between customers or transactions and other end points. Also used as a fundamental step in Weakly and Strongly Connected Components, Closeness, and other algorithms.


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