Stefano Chiartano
Word Embedding applications for Anomaly Detection in financial data.
Rel. Flavio Giobergia, Elena Maria Baralis, Danilo Giordano. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2024
Abstract
Money laundering, the process of disguising the origins of illegally obtained funds, poses a significant threat to global financial systems. Anti-Money Laundering (AML) regulations aim to prevent, detect, and report financial crimes and money laundering activities. These measures help prevent the financing of terrorism, drug trafficking, and other criminal organizations. The scale of this issue is substantial: the United Nations Office on Drugs and Crime (UNODC) estimates that 2-5\% of global GDP (Gross Domestic Product) is laundered globally. This highlights the importance for banks and financial institutions to actively fight against money laundering. However, traditional AML methodologies, which mainly consist of rule-based systems, are inadequate for the continuously evolving techniques used for money laundering.
In recent years, Machine Learning and Artificial Intelligence have been applied in this field, successfully improving the detection of fraudulent transactions and accounts
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