Part 2 of the series on fraud detection using Neo4j and graph data science delves into identifying communities of users through entity resolution (ER) and the Weakly Connected Components (WCC) algorithm to enhance fraud detection. By establishing ER rules, such as linking users who share credit cards or devices with limited connections, the analysis creates relationships that help in identifying user communities. The WCC algorithm is then applied to these relationships to define communities, which are further analyzed to label accounts as fraud risks if they include flagged users. This process uncovered an additional 211 fraud risk accounts beyond the initially flagged 241, demonstrating the effectiveness of community-based analysis in fraud detection. The identified fraud risk accounts were involved in a significant portion of the peer-to-peer transaction amounts, indicating more extensive fraudulent networks than initially detected through chargeback analysis. The findings also suggest improvements in identifying fraudulent use of cards and devices, paving the way for further exploration of graph algorithms to identify more suspicious accounts and patterns.