Financial crime, including fraud, corruption, and money laundering, poses a serious threat to the global financial system. To combat these risks, regulatory frameworks enforce Anti-Money Laundering (AML) and Combating the Financing of Terrorism (CFT) measures, requiring financial institutions to implement strict compliance controls. A key component of these efforts is Transaction Monitoring, which helps detecting and preventing suspicious activities. This thesis, developed in collaboration with the Models, Analytics, and Special Investigation office of Intesa Sanpaolo Group, proposes two proprietary algorithms to enhance transaction monitoring. By leveraging Network Analysis principles, these algorithms improve the detection of illicit activities and the quality of reports sent to the Italian Financial Intelligence Unit (UIF).