Graph algorithms are used to fight money laundering by increasing the accuracy of entity resolution, identifying high-risk payment chains and detecting networks potentially being used by high-risk accounts. The process involves creating pairwise-weighted relationships across a graph of similar entities using linear combinations of string and shared-attributes similarity scores, segmenting the graph into clusters with Weakly Connected Components algorithm, determining within each cluster which entity best represents others using Label Propagation graph algorithm, and utilizing stored procedures for Jaro-Wrinkler, Levenshtein and Sorensen-Dice algorithms along with graph attributes. Additionally, centrality algorithms detect liaisons, clustering algorithms identify subnetworks, and pathfinding and search algorithms identify payment chains and third parties layered between customers or transactions and other endpoints.