Reducibility
Glossary Contents
← Ch II
Chapter III Clique
Ch IV →
  1. 07 — Friends of friends of friends
  2. 08 — Bron–Kerbosch in C++
  3. 09 — Money laundering rings
09

Money laundering rings

Anti-money-laundering teams look for cliques in the transaction graph. Tightly-connected groups of accounts moving money in circles are how laundering looks from the outside.

Banks build the transaction graph — accounts as vertices, edges where money flowed in the last 90 days. A cluster of n accounts that all paid each other forms a near-clique, and that pattern is the textbook fingerprint of layering: cycling funds through shell entities to obscure origin. Real AML systems (Palantir, ComplyAdvantage, Quantexa) don't run exact maximum-clique on 100M-vertex graphs — they sample and approximate. They look for dense subgraphs and rank by anomaly score. The same algorithm shows up in bioinformatics: maximal cliques in protein-protein interaction graphs are candidate functional complexes. And in social-network feeds: tight friend-cliques are how Facebook decides whose photos you actually want to see.

In plain terms

If a group of bank accounts are all sending money to each other in a circle, that pattern looks suspicious. AML software searches for those tight little circles in a giant graph of every transaction the bank processes.

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