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唐朱昌
唐朱昌
教授,博士生导师。复旦大学中国反洗钱研究中心首任主任,复旦大学俄...
严立新
严立新
复旦大学国际金融学院教授,中国反洗钱研究中心执行主任,陆家嘴金...
陈浩然
陈浩然
复旦大学法学院教授、博士生导师;复旦大学国际刑法研究中心主任。...
何 萍
何 萍
华东政法大学刑法学教授,复旦大学中国反洗钱研究中心特聘研究员,荷...
李小杰
李小杰
安永金融服务风险管理、咨询总监,曾任蚂蚁金服反洗钱总监,复旦大学...
周锦贤
周锦贤
周锦贤先生,香港人,广州暨南大学法律学士,复旦大学中国反洗钱研究中...
童文俊
童文俊
高级经济师,复旦大学金融学博士,复旦大学经济学博士后。现供职于中...
汤 俊
汤 俊
武汉中南财经政法大学信息安全学院教授。长期专注于反洗钱/反恐...
李 刚
李 刚
生辰:1977.7.26 籍贯:辽宁抚顺 民族:汉 党派:九三学社 职称:教授 研究...
祝亚雄
祝亚雄
祝亚雄,1974年生,浙江衢州人。浙江师范大学经济与管理学院副教授,博...
顾卿华
顾卿华
复旦大学中国反洗钱研究中心特聘研究员;现任安永管理咨询服务合伙...
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上传时间: 2019-10-11      浏览次数:1063次
Using AI and machine learning to fight money laundering


https://timesofmalta.com/articles/view/using-ai-and-machine-learning-to-fight-money-laundering.741081

 

Data scientists have contributed a lot in making financial lives easier and, without a doubt, more secure than ever before. However, these advancements are not always used for good.

While technology has brought the financial world forward in leaps and bounds, it’s worth remembering that this same technology is used for far more nefarious reasons – cybercrime in its many forms.

Fighting money laundering is a massive, costly mission that needs to be perpetually in motion. For context, anti-money laundering (AML) measures cost European banks roughly €18 billion each year, with their US counterparts shelling out approximately €22 billion annually.

Banks all over the globe need to have their fingers firmly on the pulse of the fintech ecosystem, and that means paying close attention to their transaction monitoring standards.

Failure to do so could leave banks with a hefty fine. The last decade has seen a whopping 90% of Europe’s banks slapped with fines for failing to take the required AML measures – that’s €23 billion on fines alone.

Staying at the forefront of tech is crucial for any prioritisation hoping to remain relevant in a highly competitive industry. AI and machine learning are developing at breakneck speed, with the biggest cloud providers in the industry making them a clear priority over the last few years.

This tech has the potential to completely revolutionise the front- and back-end operations of financial bodies across the globe, boosting risk management efforts, maximising efficiency, and reinforcing the effectiveness of financial crime investigations across the board.

Embracing new technology can also minimise costs by helping financial institutions meet regulations more efficiently, which frees up human resources that can be assigned to other vital areas of the business.

There are two types of AI and machine learning, each with its own set of pros and cons – supervised and unsupervised. Supervised learning involves a model trained using data that has already been categorised to raise a red flag on any transaction that seem suspicious.

In unsupervised learning, what happens is that raw, uncategorised data is introduced to the system, making it start from scratch. By interacting with that data, the system starts to identify patterns indicative of money laundering activities while also creating new ways to sort and analyse data.

As intelligent as this tech may be, it’s only as good as the data you feed it, so investing in talent should remain a top priority. You can’t expect any model employing AI to work without any sort of human input or testing – not yet, at least.

For instance, take transaction monitoring – each transaction needs to be evaluated against a set of risk-based rules. Even the most advanced monitoring systems to date leave banks with a substantial number of false positives, and that’s when the reviewer comes in to cast a human eye over the results before making a final decision.

For any financial institution to get the best out of its human and tech resources, the two need to work hand in hand. Employees need to feel empowered to work with machines and AI, and tech needs a human element to continue moving the industry forward.

ComplyRadar monitors transactions relating to individuals, accounts, and entities to detect suspicious activity – quickly and effectively. Its machine learning transaction filter provides an additional probabilistic layer on top of the standard rules engine. This not only drastically reduces false positives, but also provides additional data output which is not being captured by simple rules.