Now robo-advisor to help Asia’s banks, asset managers to take care of customers
After the successful launch of Chloe, the first robo-advisor app in Hong Kong, 8 Securities is now offering a B2B service to large financial institutions. The new service allows banks and asset managers to customize 8 Securities’ robo-advisor platform for their own customers.
8 Securities, Asia's first robo-advisor, opens its platform to banks and assets managers to extend its service to millions of new users. On a global basis, customers are expected to deposit over $2.2 trillion in robo-advisors by 2020.
After the successful launch of Chloe, the first robo-advisor app in Hong Kong, 8 Securities is now offering a B2B service to large financial institutions. The new service allows banks and asset managers to customize 8 Securities' robo-advisor platform for their own customers. The service gives these institutions a highly flexible end to end solution to rapidly launch a robo-advisor in a matter of one to two months. The service can be launched in any geography, language and currency and is available through the web or mobile apps including iOS, Android and Windows.
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"Asia is experiencing the largest generational shift of wealth in history. However, most of the region's financial institutions do not have simple and affordable mobile wealth management solution to meet this need," said 8 Securities. "Financial services will be disrupted in much the same way Amazon has bypassed traditional retail. The importance of the branch and advisors is diminishing as more people manage their money on smartphones with the support of artificial intelligence."
How does the robo-advisor work?
Customers start by answering a short survey to set goals and a target date to achieve them. Each dollar the customer deposits is intelligently invested into a diversified global portfolio of exchange traded funds. Chloe monitors and optimizes the investment on a daily basis to help customers stay on track to achieve their financial goals. Chloe becomes more intelligent over time by using machine learning to better predict customers goal and how much they need to save.
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