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We recently had an interview with Eddy Wong, VP, Legal & Compliance of Genting in order to help attendees gain a more clear agenda of the upcoming event Big Data & AI Leaders Summit Hong Kong 2019.  In his current position, Eddy leads a team of AML specialists across multiple offices and countries, and he has designed the implementation of the company’s AML Program and developed procedures in Hong Kong, China, Macau, Singapore, Malaysia, Korea and the United States.
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Can you give your general opinions on Anti-Money Laundering? What is it and what are the risks it can bring? 
In the current AML environment, the industry as a whole is at a point where it could leverage on the current existing technologies to handle traditional procedures in customers due diligence, know your customer review and risks rating. 
These technologies can help a small AML department function in ways that will require a large amount of staff to handle the amount of researches or alerts clearing just a few years ago. As technologies advances and companies are adapting to a data-centric business model. Department like AML can leverage off existing data and paint a bigger and better picture during an investigation which wasn’t available before.
In terms of risk, the traditional focus of AML mainly connected to financial institutions and banks but lots of other businesses are emerging to present the same risk as a target for money laundering organization.
In your experience, how Artificial Intelligence has helped you in combating financial crimes?
In my current experience, true AI had not been deployed in the financial industry. Machine learning and other similar techniques of AI had been implemented with some limitation but it is very promising to see these new technologies being deployed in a real-life situation. I had seen and used tools that are very capable to provide current compliance department ways to saved cost and time in repetitive tasks. 

Many financial services companies use AI to fight financial crimes. However, a feasible technique does not mean a company is in a position to apply immediately. How do you suggest these companies to handle the technology?
The problem of new technology is adaptation and integration into existing systems and infrastructure. Most companies have legacy systems, upgrading and adapting new technologies within old and sometimes outdated infrastructure is a long and sometimes painful process of growing. For some, the most successful project will take the most time and resources since it’s a core change in how a company functions.  Systematizing each area of business to adapt to a new way of doing old repetitive tasks which most are eliminated. Current staff will need to find new ways to enhance and adapt to the current progression of their day to day tasks.
Training and enhancing the skills of the current staff will be a great way to adapt to the requirements of change management. In addition to training, hiring internal staff with knowledge of data sciences enable a small team to handle larger amount of work by levering on new technologies. 

Your experiences involved in a lot of Asia and the US regions. Do you think similar strategies, implementation and technology can also be applied in Europe, whose countries started to appear to have high risks countries of money laundering?
Yes, we all experience similar money laundering typologies around the world in the same old industry of institutionalized money laundering organization. Technologies and techniques of handling these risks can be localized to the jurisdictions based on the business practices. Based on the emergence of a new way to exchange wealth such as cryptocurrencies in a loosely regulated environment, organize group often take advantage of the loophole until traditional measures catch up to the new technologies. 
Based on the current developing trend of enforcement, firms with no compliance program are often used as an example of enforcement by name shaming and large fines. Higher risk countries and industries are often targetted for these organizations to utilize in different stages of money laundering. These criminal enterprises will leverage the lack of oversight and controls to introduce their illicit funds into the financial system. Then utilize different methodologies to transfers and transact to separate the true source of fund. 
Different countries in Europe where they appear to high risks are also a target for money launder because they often find the path of least resistance. Valuable assets like precious stones and precious metals, real estate and high-end art are also used due to the lower requirement to the verified source of funds but regulation and enforcement are catching up.  

Apart from combating financial crimes, how can AI be used to increase the companies efficiency - in terms of account management, data security?
AI is often great uses case for repetitive tasks. Account management and audit are great for these tedious tasks. AI efficiencies and transparency provide accounting team ways to leverage resources to deal with larger amount of work in a shorter amount of time. It enables staff to focus on problems instead of repetitive, low-value tasks. 
As for data security, AI enables a small team of professionals to manage a large number of incidents without extra resources. Each team member could specialize in their areas and focus on actual risks instead of chasing down false leads and incident logs.  

Any challenges in managing a client’s data privacy using AI? 
The challenges of managing clients data privacy are to find a good use case to demonstrate a value-added system to justify the cost. Often new technologies come with a large price tag and adaption is often difficult and at best partially integrated. Due to the difficulty, the best-case scenarios is to take the project apart and adapt to the path of least resistance with most management buy into the new technology. 
Eddy is the speaker at the upcoming AI & Big Data Leaders Summit Hong Kong 2019. To find out more about his presentation, please click here.

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