Transformer Representation for Money Laundering Detection скачать в хорошем качестве

Transformer Representation for Money Laundering Detection

This deep dive discusses a 𝗻𝗼𝘃𝗲𝗹 𝘁𝘄𝗼-𝘀𝘁𝗲𝗽 𝗺𝗲𝘁𝗵𝗼𝗱𝗼𝗹𝗼𝗴𝘆 for 𝗺𝗼𝗻𝗲𝘆 𝗹𝗮𝘂𝗻𝗱𝗲𝗿𝗶𝗻𝗴 𝗱𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 that significantly improves upon existing rule-based and traditional machine learning methods published in the paper 𝙍𝙚𝙥𝙧𝙚𝙨𝙚𝙣𝙩𝙖𝙩𝙞𝙤𝙣 𝙡𝙚𝙖𝙧𝙣𝙞𝙣𝙜 𝙬𝙞𝙩𝙝 𝙖 𝙩𝙧𝙖𝙣𝙨𝙛𝙤𝙧𝙢𝙚𝙧 𝙗𝙮 𝙘𝙤𝙣𝙩𝙧𝙖𝙨𝙩𝙞𝙫𝙚 𝙡𝙚𝙖𝙧𝙣𝙞𝙣𝙜 𝙛𝙤𝙧 𝙢𝙤𝙣𝙚𝙮 𝙡𝙖𝙪𝙣𝙙𝙚𝙧𝙞𝙣𝙜 𝙙𝙚𝙩𝙚𝙘𝙩𝙞𝙤𝙣 by Harold Gueneau, Alain Celisse (Laboratoire SAMM, Universite Paris 1 Pantheon-Sorbonne, Paris) and Pascal Delange (Marble). The first step involves 𝗿𝗲𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 using a 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿 𝗻𝗲𝘂𝗿𝗮𝗹 𝗻𝗲𝘁𝘄𝗼𝗿𝗸, which analyzes complex financial time series data without requiring labels through 𝗰𝗼𝗻𝘁𝗿𝗮𝘀𝘁𝗶𝘃𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴. This 𝘀𝗲𝗹𝗳-𝘀𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗽𝗿𝗲-𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 helps the model understand the inherent patterns in transactions. The second step then leverages these learned representations within a 𝘁𝘄𝗼-𝘁𝗵𝗿𝗲𝘀𝗵𝗼𝗹𝗱 𝗰𝗹𝗮𝘀𝘀𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗽𝗿𝗼𝗰𝗲𝗱𝘂𝗿𝗲, calibrated by the 𝗕𝗲𝗻𝗷𝗮𝗺𝗶𝗻𝗶-𝗛𝗼𝗰𝗵𝗯𝗲𝗿𝗴 (𝗕𝗛) 𝗽𝗿𝗼𝗰𝗲𝗱𝘂𝗿𝗲, to control the false positive rate while accurately identifying both fraudulent and non-fraudulent accounts, addressing the significant class imbalance in money laundering datasets. Experimental results on real-world, anonymized financial data demonstrate that this 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿-𝗯𝗮𝘀𝗲𝗱 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 outperforms other models in detecting fraudulent activities.