Financial Fraud Detection Using Advanced Algorithms

Financial fraud, particularly through complex networks involving mule accounts and shell companies, poses a growing challenge to regulatory agencies and financial institutions. Traditional rule-based systems often fail to detect such concealed fund movements due to their inability to capture relational and structural anomalies in transactional data. This paper presents a graph-based analytical framework for fund trail analysis and fraud detection, leveraging classical algorithms such as Tarjan’s for strongly connected components, Ford-Fulkerson for flow computation, and Louvain for community detection. The proposed system processes multi-format financial data, constructs a transactional graph, and applies anomaly detection techniques to identify high-risk accounts, circular fund flows, and coordinated fraud patterns. To support forensic investigation, the framework incorporates visual analytics for interactive exploration of suspicious account clusters and flow paths. A vulnerability scoring model is introduced to rank accounts based on their deviation between direct and indirect fund transfers. The system was implemented using Python (NetworkX, Scikit-learn), Neo4j for graph storage, and Flask for web-based deployment. Comparative evaluation against traditional transaction monitoring methods demonstrates improved interpretability and detection capability in simulated money-laundering scenarios. This work contributes a scalable, transparent, and investigator-friendly solution for enhancing fraud intelligence in financial networks.