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is important to employ new approaches to tackle the evolving
challenges around financial crime.
Some of the main forms of technology enablers that lead the market trend and that are changing the way work is conducted include the following.
Artificial Intelligence (AI), which refers to machines that
can mimic human cognition and take on tasks that require relatively complex reasoning and decision making. AI can help to automate business processes, detect patterns in criminal behaviour, generate insights, and engage customers and employees through routine communications. This technology is being used to enhance customer due diligence (CDD) and enable know-your-customer (KYC) processes by making them faster and producing more accurate AML data, which allows an organisation to conduct thorough risk assessments.
Machine Learning leads the trend of technology, where it enables continuous improvement of a model, allowing the effective capture of subtleties and dynamism around criminal and compliance risk behaviours, which are almost impossible to code effectively under a rules-based approach. This can
be considered a subset of AI. The machine learns to grasp patterns in data or tasks beyond its pre-defined coding, therefore facilitating more accurate and predictive analytics from large, complex data sets. This provides significant adaptability to new threats and methodologies. Machine learning is particularly relevant for money laundering and terrorist financing (ML/TF) transaction monitoring, due to
its ability to make judgements about criminal behaviour, increasing the accuracy of its risk assessments and thus reducing the risk of false positive alerts (falsely alerting teams of suspected improper behaviour).
Natural Language Processing (NLP) is another subset of
AI that allows systems to recognise and interpret meaning from human languages. NLP enables machines to process
and understand large volumes of unstructured data such as news articles, emails and social media posts. From a financial crime perspective, the machine is able to read and compile information written about an individual or organisation, consider the context of the information, and form judgements as to whether or not the individual or organisation is suspicious. Therefore, NLP can also support Suspicious Activity Report (SAR) or Suspicious Transaction Report (STR) processes through the automatic generation of reports with standardised terminology and language, reducing a firm’s administrative burden and ensuring a consistent approach.
務程序自動化、偵測犯罪行為模式、觀察現象, 以 及 透 過 日 常 溝 通 ,與 客 戶 及 僱 員 聯 繫 。金 融 機構目前利用人工智能加強客戶盡職審查和 認識你的客戶的工作,使過程更快捷,製作更 精準的打擊洗錢資料,讓機構作全面的風險 評估。
機器學習帶領着科技發展的潮流,讓智能工具 持續改進,從而有效地掌握犯罪及合規風險行 為的蛛絲馬跡,緊貼這些行為的變化;這些微 小的細節和變化,是幾乎不可能有效地以規則 規管的。機器學習可以視為人工智能的分支, 通過掌握事先界定的編碼以外的數據或任務 的模式,從而對龐大複雜的數據集作更精準和 預測性的分析,大大有助應對新威脅,配合新 的做事方式。由於機器學習能判別犯罪行為, 因此特別適宜監察洗錢及恐怖分子資金籌集 的交易,有助提高風險評估的準確度,減低錯 誤警示(錯誤懷疑有合規人員不恰當的行為) 的風險。
自然語言處理技術(NLP)是人工智能的另一 分支,讓資訊科技系統可辨識和解讀人類的語 言。NLP讓機器處理和了解大量未經組織的數 據,例如新聞報道、電郵和社交媒體帖文等。從 金融罪行的角度看,機器可閱讀及編輯有關個 人或機構的文字資料,考慮資料的背景,並判 斷 該 個 人 或 機 構 是 否 可 疑 。因 此 ,N L P 也 可 藉
ISSUE 119 • 2021
“The trend towards digitisation, which has been accelerated by the economic landscape and repercussion from the COVID-19 pandemic, is changing the typology
of financial crime
and the way in which law enforcement and regulated entities seek to detect it.

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