Robo-Advisors Permeate Asian Markets

Robo-advisors are gradually becoming more popular in Asia, especially among retail investors. While it will take some time, these advancements—and the technologies that underpin these platforms—are likely to seep into the institutional space in years to come. Wei-Shen Wong speaks to several firms in the region to discuss how they’ve built out their robo-advisory platforms and what that could mean for the buy side going forward.

Robo-advisory platforms are gaining steam in Asia-Pacific, and might prove a disruptive force to the asset management industry in the region in the years to come. And while the growth in this sector is being driven by retail investors, from a technology perspective, it will seep into how asset managers deal with institutional clients.

A primary reason for this trend is the growth the region is seeing in investable wealth. BI Intelligence expects the region to record $2.4 trillion in robo-advisory assets under management by 2020. These platforms are becoming more popular among retail investors because they automate the investing process. This automation is underpinned by artificial intelligence (AI) techniques, in particular machine learning and natural-language processing (NLP).

If this trend continues, it will only be a matter of time before institutional investors show more interest in being part of the action. At the worst, these platforms can serve as models for how buy-side firms will interact with their institutional clients going forward.

According to consulting firm PwC, global assets under management—including robo-advisory and traditional asset management—are estimated to hit $101.7 trillion by 2020, from $63.9 trillion in 2012. Of this, Asia-Pacific assets under management (AUM) will take up just under 16 percent of the total, compared with 12 percent in 2012. Additionally, high net-worth individuals (HNWIs) in Asia-Pacific are estimated to account for 29 percent of the global total in 2020, up from 24 percent in 2012, while the mass affluent in the region will see 9 percent growth in that timeframe. 

PwC writes in its A Brave New World report that between 2010 and 2020, more than 1 billion additional middle-class consumers will emerge globally, representing the largest single-decade increase in customers. By then, Asia will have replaced Europe as having the largest middle-class population.  Standard Chartered, in its Asian Asset Management’s Inflection Point report, published last year, expanded on that: “Robo-advisors might encourage longer-term investment in more complex products. They customize savers’ investment portfolios in a way that is simple, low-cost and objective. As international financial institutions finally overcome the difficulties lingering from the financial crisis, they are focusing on wealth management. Asia’s growing wealthy are a natural destination, and robo-advisors a natural distribution tool.”

Robots vs. Humans

One of the factors supporting the rise in popularity of robo-advisors is that minimum investment figures are a lot lower than the amounts required to invest with a traditional asset manager. A traditional asset manager would not target households primarily because the human cost of delivering advice is high. The infrastructure was built for high-value, low-volume investments and not vice-versa. Still, the average retail investor needs access to quality advice and financial products. This is where robo-advisors play a vital role, says Leo Chen, head of Asia for vendor Calastone. This is particularly in line with the rise of the middle class in Asia. In China, it’s anticipated that in 10 years there will be 300 million people classified in this category. “They will have money to save and invest. Digital and mobile technology will also drive this in China and in the rest of Asia,” Chen says.  One of the main arguments for investing with robo-advisors versus traditional asset managers is that the latter have been unable to outperform their benchmarks after fees, according to Artur Luhaäär, co-founder and CFO at Smartly. 

“For the past eight years, the simple passive portfolio has outperformed an active fund,” he says. “In Singapore’s case, we offer a digital solution that can be set up within minutes, is hassle free and for a fraction of the cost of existing solutions. In addition, we’re fully transparent about our costs and investment methodologies. There are no upfront fees and you only pay for the time you have invested your money. It’s also important to mention that we do not receive any commissions from the products we sell, so we are independent to pick the best products for our customers. This cannot always be said about financial advisors.”   Smartly charges its clients a 0.7 percent fee, whereas average management fees in the industry range between 2 percent and 3 percent, annually. Smartly’s service is only available to Singapore investors and employment pass holders. However, it aims to expand its reach to Southeast Asia and is planning on making its platform available in Malaysia. 

Incorporating AI

Hong Kong-based robo-advisor 8 Securities created an app, dubbed Chloe, with the aim of replicating the whole process of an investor walking into a bank and sitting down with a relationship manager, but doing that digitally. “If you have a robo that still requires you to go and see someone to complete the investment process, that’s not a robo anymore,” says Mikaal Abdulla, CEO at 8 Securities.  But how exactly does a robo-advisor platform work? What technology is used to automate the investment process? Abdulla says Chloe gathers a lot of data from potential investors, allowing it to predict a person’s investment goals based on how they answer a few simple questions.

“We have the persona—Chloe—and financial plans based on people’s life events and also the ability to connect with them using machine learning and cognitive services. Those two things are separate, even though they share common ground,” says Cedric Roll, CTO at 8 Securities. “Cognitive services are more along the lines of creating that human relationship like recognizing voice, understanding texts, and extracting sentiments from that—basically replicating those human interactions. Machine learning is all about resolving a problem by predicting an outcome based on past interaction or past data that we can collect.”

He adds that using machine learning within Chloe paints a better picture of how achievable those goals are by looking at what others have realistically achieved. “If you are looking to save for a property, we are going to look at the property market in Hong Kong and we’re going to give you some context on what the property market in Hong Kong might be. We will ask you what your income is and based on that income, this is the type of mortgage you can afford, and so on. That then becomes real and local as well. We are looking to do that for all other goals too,” he says.

  8 Securities’ application was developed using Ionic 2, an open-source software development kit. “It allows us to develop the application once and then release it across all the app stores and to all devices very fast,” Roll says. “This way it is able to interact with the user experience and get clients’ feedback, change the application and then release it without having to develop it in C or Java or any other crazy language—one source code, all the channels.”

The application uses the architecture of Microservices, a recent architectural program for applications, which serves the user interface. 8 Securities also integrated with third parties, allowing it to take content and services and then build within Ionic Material to create a consistent experience across all touch points.

8 Securities is looking at using different services like Python to be able to leverage its machine-learning algorithm and do it internally. Roll adds that the company is working with key partners including Microsoft, and has spent time developing prototypes together. “We have been using their cognitive services for building a chatbot to interact with the client. If we move forward with that, clients will be able to interact with Chloe via Skype, Messenger, etc., and we will be able to guide them using the chatbot,” he says. The firm is also in talks with Google and Amazon for potential services.

Existing clients now interact through the app with 8 Securities’ customer officers. The chatbot will use a natural-language processing engine that will extract intent and tag topics of conversation the client had with the bot, although it has yet to set the chatbot’s dialogue flow.

New Exposure

In May this year, Farringdon Group, based in Singapore, will launch Asia’s first Sharia-compliant robo-advisor, named Algebra. It will launch initially in Malaysia with a minimum investment of $200 per month and will offer users one of five different risk-weighted strategies to create a portfolio of Sharia-compliant funds, says Martin Young, CEO of Farringdon Asset Management.  It will use a platform called Replicas from Singapore-based technology vendor Replication Technologies, which will allow it to track the top 10 fund managers in the US to create portfolios for Algebra users. Young says it is essentially an algorithm that identifies which stocks fund managers are overweight in. The algo is used to determine which stocks they effectively hold and which ones they hold purely for diversification purposes, he says.

“The way we’ve designed this thing is unique, but it’s unique by being light on the tech side in terms of execution,” Young says. “Because we run it internally, and we are licensed to do that in the same way that a bank is, we don’t need to communicate with other people’s systems directly. We have an algo that determines our hedging position in terms of how much stock or how many assets we have to buy across the entire book for everyone.”

He says the company still uses humans to oversee the algos and there is still some human intervention. The internal system shows which positions it needs to hold as a company, although that is manually done on an hourly basis with brokers buying stock on behalf of the company.

“That’s quite different from what other companies are doing, and certainly here in Singapore where they are interacting with a custodian, which is interacting with a stock broker. The other reason we do that is that we can cut the account minimums much lower than what any custodian would accept,” he says. “Like the custodians here in Asia, there’s limited choice. With a segregated custodian account, they probably want a minimum client size account of $50,000 to $100,000, whereas we are targeting a minimum $200,” he adds.

Meanwhile, BetaSmartz, a business-to-business automated investment platform offering hybrid robo/human advice, uses an open architecture in its platform. This allows clients—banks, private banks, financial advisors and asset and fund managers—to customize everything from their risk profile through to execution and trading, as well as portfolio construction and asset allocation coming from clients.

John James, founder of BetaSmartz, says what makes the platform stand out is that it uses much more “deep data” to advise clients than most robo platforms. “That ranges from government data to private sources and also data shared by the client,” he says. “We do things like allow the client to aggregate their third-party accounts so that we have a bigger picture of their wealth,” he says.  BetaSmartz also uses machine learning and natural-language processing in its advice delivery. James adds that it is now looking at potentially integrating certain Internet of Things (IoT) technologies around the advice it delivers, particularly in terms of financial wellness. “We are looking at financial wellness of an individual in the broader sense and that includes their health and wellbeing,” he says.

Challenges Still

Although more robo-advisory platforms in Asia have been popping up, it is still a relatively new concept. Before catching up with the AUM of Western robo-advisors, the investing public needs to become comfortable with the concept and existence of robos and then to the benefits, says Charlie O’Flaherty, partner and head of digital strategy & distribution at wealth management firm Crossbridge Capital.

“Once the efficiencies are made clear and robos become part of the local lexicon, we might experience an accelerated adoption rate,” he adds. However, O’Flaherty says Asia has the luxury of being able to learn lessons from other markets. “For instance, many of the initial robo platforms in the US and Europe employed a more passive approach to their portfolios. When we built Connect by Crossbridge, we envisioned a platform that would utilize technology to enable an intuitive means of actively managing globally-diversified portfolios,” O’Flaherty says.

Connect serves accredited investors living in Singapore, including US expat investors, whose net personal assets exceed $1.4 million or whose income over a 12-month period is not less than $214,000. “We’ve also taken a leaf from some of the more successful advisors overseas to introduce factors like goals-based investing, which gives investors a much greater degree of portfolio customization to suit different investment needs.”

There are other factors that could hold robo-advisors back, notes Farringdon’s Young. For example, Asia is a fragmented marketplace and there is a lack of choice in terms of asset management tools and quality assets. “We also don’t have a massive selection of exchange-traded funds (ETFs),” Young says.

Particularly for those offering a white-labeled robo-as-a-service, the issue for them is in terms of custodians, which expect high fees. “Some of them have expense ratios of 1.5 percent, which for us is high. For a robo-advisory to be successful here, it needs to be 0.7 percent or lower, including ETF charges and custodian charges. That seems to be quite difficult to achieve in Asia,” he adds. For this reason, Young says 90 percent of robo-advisors will struggle to generate profits because they would need $100 million in AUM just to break even.

Tech Revolution?

Even though Asia has a lot of catching up to do in terms of broadening these outlets’ appeal, it has significant growth potential. The distribution landscape in Asia is changing rapidly, says Calastone’s Chen. “With robo-advisors coming into play, a higher level of automation in fund transaction is necessary. The robo-advisory model is a self-service model. After advice is given and a fund is selected, the purchase of the fund needs to be processed automatically. However, in Asia, many trades are still processed manually,” says Chen.

But the region is rapidly catching up in fund market automation, driven by cross-border fund “passporting” schemes, such as the Mutual Recognition of Funds, a China–Hong Kong joint venture launched by the China Securities Regulatory Commission (CSRC) and Hong Kong Securities and Futures Commission (SFC). Chen expects many more asset managers to add robo-advisory offerings. By doing so, asset managers will be able to gain direct access to clients, which they currently do not have. “Asset managers have traditionally relied on distributors who hold relationships with clients. The current market model presents challenges to the asset managers on two fronts,” Chen explains.

A consequence of this is that asset managers will have to rethink their client servicing models, including direct customer service and know you customer (KYC) functions, which can be quite costly. “In the fintech age, young consumers seem more interested in going directly to the source, getting advice and purchasing investment products straight from the manager,” Chen continues. “With the rise of robo-advisory, asset managers will be able to interact with their clients directly. Asset managers may only have around 100 traditional B2B clients; by expanding into retail, they can grow that base to include 20,000 B2C clients. This is where the new challenges come in. They will need to pick up clients’ calls, manage KYC information and provide retail customer services, among other things,” says Chen.

The Way Forward

One needs only to look at the adoption of cloud technologies to see how the robo advancement in the retail space could yield major dividends in the institutional asset management industry. Ten years ago, buy-side firms were reluctant to even consider building an internal, private cloud to test models on, much less turn over critical data to a third-party cloud provider. And five years ago, while cloud adoption started to increase rapidly, public clouds were shunned. Now, more and more institutional asset managers are putting their information in the cloud using Amazon Web Services and the Google Cloud Platform. Meanwhile, on the retail side, all those milestones were hit far sooner than the industry has witnessed on the buy side.

Similarly, while robo-advisory services are currently the domain of the retail asset management market, expect to see some of those platforms and their underlying technologies—machine learning and natural-language processing, for example—adopted by institutional organizations to improve their investment decision-making, enhance their mobility, expand their regional presence, and find new avenues for alpha generation.

The retail banking industry has traditionally outpaced the institutional market when it comes to innovation. But as European and North American firms look to find new investment opportunities in the Asia-Pacific region, it will be vital for them to better understand the marketplace as a whole—a marketplace that will increasingly make use of robo-advisors.

Source from