Artificial intelligence, automation and algorithms drive fintech
Agrowing number of startups have disrupted traditional industries in recent years. The noteworthy ones include ride hailing service providers in the transportation industry, where US-based Uber, China’s Didi Chuxing, Singapore-based Grab and Indonesia’s Gojek are changing the way people travel daily.
In the hospitality sector, the emergence of Airbnb has allowed homeowners to rent out their spare rooms or entire homes to travellers, opening up a wide array of accommodation choices for vacationers.
Meanwhile, Netflix has revolutionised the entertainment industry by offering movies and television shows at a fraction of the cost of pay-TV subscriptions.
Driving the success of these companies are the three As: artificial Intelligence (AI) and automation, which are powered by algorithms, where computers use a process or set of rules for calculations and other problem solving.
A Pricewaterhousecoopers (PwC) survey in 2016 found that only 45% of asset managers put financial technology (fintech) at the heart of their investment strategy, and 34% did not deal with fintech firms at all. The survey suggested that asset managers were lagging other industries in adopting fintech.
But that seems to be changing. A recent PwC survey found that chief executive officers of asset and wealth management companies see “huge potential” in technology and recognise the importance of data and analytics. Some 90% of CEOs agreed or strongly agreed that AI will significantly change the way they do business in the next five years, according to a report on the survey published on March 13.
Power of data
Gordon Brown, managing partner at Singapore-based asset management consultancy Stradegi Consulting, says active managers are under pressure from passive investment strategies. So they are looking for ways to innovate and to grow their product offerings and develop operating models that can efficiently deliver their investment propositions to clients.
“We are seeing more of a demand for investment managers wanting to understand new opportunities to update their technology architecture and improve data utilisation, which is often unstructured and fragmented,” Mr. Brown says.
In the 2019 report, PwC points out that the key to improving productivity is better data management. It describes data as the “21st-century fuel” businesses can use to be “faster, smarter and more responsive to market conditions”.
In order to be successful, companies will need to share data and information throughout an enterprise, and to leverage market insights and customer feedback faster. Gone are the days when individual departments kept and guarded their own data.
“It’s not simply a matter of applying technology – it’s a matter of moving away from the status quo that sees islands of technology and data within a firm,” the report says.
The rise of robo advisers
Robo advisers, which provide financial advice and/or investment management online with moderate to minimal human intervention, first emerged in the US over a decade ago, during the global financial crisis. Their services are based on computer algorithms, automation and AI.
Robo advisers ask investors a series of questions to determine risk appetites and guide them to products that suit their financial goals. They use AI technology to analyse historical trends and “predict” outcomes by spotting correlations between certain key data metrics and market performance. And by using automation, robo advisers are able to make buy or sell decisions when certain key indicators are met, such as selling stocks when inflation hits a certain threshold.
New York-based Betterment, established in 2008, was one of the first robo advisers in the market. Today, it’s one of the largest around, with US$16.4 billion of assets under management (AUM) as at April 1, 2019. One of its closest rivals, California-based Wealthfront, which also launched its service in 2008, had $11.4 billion of assets as of January 2019.
Robo advisers only started to make waves in Asia more recently. One of the more notable ones in the region is Singapore-based StashAway, co-founded by former Zamora Group CEO Michele Ferrario, Freddy Lim, a former managing director at Nomura, and entrepreneur Nino Ulsamer.
StashAway launched services in Singapore in 2017, and in Malaysia a year later. The company does not publicise its assets data, but given that it’s a relative newcomer, the figure is unlikely to be anywhere close to Betterment.
Despite their significant growth, robo advisers still only command a tiny slice of the asset management industry. Betterment and Wealthfront have combined AUM of around $30 billion – or just 0.5% of BlackRock Inc’s assets. BlackRock, the world’s largest asset manager, had AUM of $5.98 trillion as at December 31, 2018.
Mr. Brown worries that this kind of lag may discourage investment managers from continuing to invest in new technology.
“We understand the problem and we’ve faced it ourselves while building our data science practice. It took years of mistakes, learning and improvement to reach the market-leading standard we can proudly talk about today,” he says.
Real life adoption
Despite his concern, many asset managers are showing good progress in fintech innovation. Lee Bray, Asia Pacific head of equity trading at JP Morgan Asset Management, says his company has invested significantly in building machine learning tools to enhance global equity trading as the rise of AI and automation “transforms how we conduct business”.
The company’s Asia Pacific buy-side equity trading team has developed a model using machine learning to help its portfolio managers execute trading orders in a more effective and cost-efficient way.
“The proprietary model, which was developed by our quantitative analysts and traders, uses data patterns to find the optimal execution strategy for trading orders. Our Asia Pacific trading desk is on track to have more than 50% of equity trading flow driven by machine learning by end of 2019,” Mr. Bray says.
He says the AI model “learns constantly about the best outcomes for trading orders and adapts by recalibrating as market conditions change and new information is delivered”.
According to Mr. Bray, in order to develop the AI model, the firm taps into “techniques more commonly found at companies like Facebook and Google”.
“By creating a systematic, adaptive model able to alter actions based on mathematical patterns rather than relying on human input, we are transitioning equity trading to be more scientific and quantifiable,” he says.
Andre Jaekel, co-head of digital at German asset manager DWS, says technology adoption has helped the company transform its business model in Asia. In addition to providing funds, discretionary portfolio management or financial advice to investors, DWS now also sells its software, called WISE, to distribution partners. The software allows the distribution partners to offer discretionary portfolio management services to their clients, and provide strategic asset allocation and portfolio solutions to investors.
“This generates additional revenues on top of our existing traditional sources of income. Hence we act as a fintech more in a sense of generating revenues rather than focus on procuring fintech products to save costs,” Mr. Jaekel says,
The software is now in use by Taiwan’s Ezfunds Securities Investment Consulting Enterprise Ltd. The firm, which operates the Ezfunds online fund trading platform in Taiwan, used the software to launch WISEGo, which offers robo advisory services to domestic investors.
WISE is largely powered by AI. “We use for instance AI and big data tools to look into web traffic patterns, for instance of agents using our robo advice platform,” Mr. Jaekel says.
At Stradegi, the firm began building a dedicated, in-house data science team in 2016 as a way to differentiate itself from rivals.
“In 2018, our data science and consulting team helped give our clients a better understanding of how to leverage their data and technology to uncover actionable insights. We facilitated workshops throughout the year to help investment managers make sense of AI, machine learning and other commonly used buzzwords,” Mr. Brown says.
Fintech companies face a number of challenges in trying to convince asset managers to adopt new technology.
According to Becky Chiu, director of client relationship management at fund transaction network provider Calastone, some asset managers have a “if it is not broken, don’t fix it” mentality, especially on improving information technology infrastructure.
“This is where I believe that a change of mindset is important, and will need to be driven and supported by senior management,” Ms. Chiu says, adding that it’s important for them to understand there are “downstream costs for not upgrading their infrastructure and adopting new technology”.
“For example, in the process of manually keying in trading data and orders onto a system, there’s a high likelihood of human errors and increase in operational risks, resulting in penalties, fines, and reputational damage,” she explains.
Mr. Brown believes the biggest challenge for asset managers is to get the right expertise for their digital transformation journey.
“Today’s market is full of fintech noise to filter. Finding the right people, solutions or partners in such a rapidly evolving landscape makes thoroughly evaluating your options critical to success,” he says.
But such changes are not going to happen overnight. It takes time to convince companies to make significant changes to their IT infrastructure.
“In order for us to overcome the challenges of convincing distributors and asset managers of the merits of adopting new technology, we believe it is important to help them understand the long-term benefits that it brings, and the opportunity costs it entails,” Ms. Chiu says. “For example, adoption of new technology can improve operational efficiency. Instead of spending time on manual fund order processing, employees can now use the time to focus on value-added activities, such as improving client experience and quality of service.”