The article explores the use of Neo4j and Graph Data Science (GDS) for fraud detection, specifically in identifying high-risk users and expanding on existing business logic to automatically identify suspiciously similar users. It leverages centrality and similarity algorithms to generate a ranked list of potential high-risk accounts using weighted degree centrality, which calculates the degree centrality of users based on their identifiers weighted by the fraudRiskRatio values. The article also uses node similarity algorithms to expand on existing communities of fraud risk users, identifying pairs of similar nodes based on Jaccard similarity calculations and projecting the graph to write relationships back to the database with a score representing similarity strength between user node pairs. These methods can help automate and semi-supervised processes for targeted triage and identification of suspicious user accounts based on previously labeled data.