近年來，我國在洗錢防制（Anti-Money Laundering, AML）的立法及執
行上逐漸邁入正軌並與先進國家逐步看齊，國內金融機構多數都已建置基本
洗錢防制系統，並配合商業資料庫提供的名單進行認識你的客戶（Know
Your Customer, KYC）與客戶盡職調查（Customer Due Diligence, CDD）中的
姓名檢核工作。然而，在實際作業中，金融機構因姓名檢核缺失被主管機關
糾正或裁罰案例層出不窮，此皆肇因於姓名檢核作業的困難性以及工作的繁
重，法遵洗防單位苦於沒有科技協助只能大量仰賴人力所致。
本文依據國內實際實施的困難並針對現況進行分析，並參照國外的實際
案例作法，以及符合國內情境方式引入新技術來提出建議，期望能為金融機
構在採用 AI 大數據等新技術執行洗錢防制負面新聞檢核時作為參考。

In recent years, our country has gradually stepped on the right track in the
legislation and implementation of anti-money laundering (AML), and has gradually
lined up with advanced countries. Most domestic financial institutions have estab-
lished basic anti-money laundering systems, and perform the name screening oper-
ation of know your customers (KYC) and customer due diligence (CDD) process
with the sanction and watchlists provided by the commercial database. However, in
practice, financial institutions have been corrected or punished by the FSC
(Financial Supervisory Commission R.O.C. (Taiwan)) due to the lack and missing
of name screening. This is due to the difficulty and heavy workload of name
screening operation. The legal compliance team suffers from the lack of
technological assistance. It can only rely heavily on manpower. Based on the difficulties of domestic actual implementation, this paper analyzes the current situation, refers to the actual cases and practices of foreign countries, and introduces new technologies in line with the domestic situation, hoping to beAreference for financial institutions when using AI big data and other new technologies to carry out the inspection of anti-money laundering of adverse media screening.