Static analysis and metaheuristics for blockchain-focused fraud detection
Otilia Muntean, PhD student in Computer Science
Abstract: In the blockchain forensic research space it is often discussed about the scarcity of anti money laundering techniques and the difficulty of adapting the technologies to the current market demands. We are taking a closer look at the existent problems and the available solutions, by taking a deeper dive into the current technical and legislative regulations. We conduct a review study based on existent approaches used for fraud detection and we outline the importance of machine learning and artificial intelligence for developing scalable and reliable techniques. The main goal is to provide a stronger focus towards achieving money laundering detection, eventually using one or more approaches to support the collaboration between the financial and technical areas. The problem of financial crimes in blockchain-focused systems is divided between the regulations, policies and laws of the areas representing the two endpoints of a transaction, as well as the technical vulnerabilities or weak aspects of the blockchain which are exploited by cybercriminals.