Will the Wealth Management Industry Leverage the Full Potential of Data and AI?

Panel participants gathered at the Hubbis Digital Wealth Asia Forum to discuss the current artificial intelligence and big data, highlighting why so many people in the wealth management industry are not yet fully on board with what these digital behemoths can do to improve the sector’s productivity and profitability. Understanding how to leverage data properly through customer analytics is a vital key to unlocking the full potential of that expensively assembled wealth management client base.

The Key Takeaways

Moving past the fear of obsolescence

Panellists agreed that AI and machine learning should not be seen as a threat, but instead can be harnessed to bring real benefits to the wealth management industry.

Technology has advanced to meet demand, to a point

As computational models have advanced and quantum technology means computing power on a truly massive scale, it is not enough to collect just data without any thought to the way in which it will be used. Ask the right questions, panellists advised, and you will get useful answers from the data that will boost your business.

The scale of the analytics challenge is no longer prohibitive with AI

Customer data and real-time portfolio management require a truly gargantuan amount of information processing power to keep up with market trends. AI can sift through this mountain of data with increasing ease and generate useful outputs that can significantly enhance the capabilities of the relationship managers and the credibility of the providers themselves.

Large AI uptake spectrum in wealth management

Panellists explained that while some companies are still burying their heads in the sand when it comes to AI and big data, others are testing the waters using third-party data analytics organisations and some are already racing way ahead of the competition, even this early in the game.

Boosting capabilities and enhancing efficient

Without AI, many wealth managers are indeed diligent, educated and even sometimes brilliant when offering their wealth management clients advice and making investment decisions. However, with AI augmentation, decision-making is based on algorithms that provide valid support for both advice and decisions, leading to more consistent and potentially higher returns.

AI is core to the future of wealth management, don’t ignore its potential

The panel agreed that AI will change the way wealth and asset managers operate, and therefore lead to lower entry barriers, lower costs for the customer, greater competition from technology startups, and the leveraging of customer and market data to provide increasingly personalised service and higher returns. Those who ignore this will do so at their peril.

The Discussion

Panel members gathered to tackle some big questions in the field of artificial intelligence (AI) and big data. The first issue discussed was how these analytical tools can save money and drive revenue. For example, a panellist said that they can be used to find the best hedge for any portfolio by analysing threats, gauging impact and trawling markets with minimum effort and expense while also saving manpower.

It is important to remember, the panel reassured the audience, that they must see past the hype, and that AI is not ‘taking over’ their jobs, and that there is a natural evolution of supportive technological advancements. AI and big data will ultimately benefit the financial services industry by enhancing existing operations, boosting individual capabilities and efficiency, and thereby creating new opportunities for both the clients and the providers.

A good robot never complains

The speed of progress in both hardware and software means that digital systems can handle a truly massive scale of computation, and this is vital as data, like the universe itself, is ever-expanding.

“This means that huge volumes of data can now be processed by robots at the speed of light and also a sensible cost, and what’s more, the robos make no complaints and never need breaks,” a panellist quipped.

Next, the panel explained that it is not enough to collect all the data, but to be selective, to ensure the right questions are asked and answered. “First, understand the goal, then work backwards in a back-to-front process to identify the suitable dataset and corresponding analytical algorithm to derive useful insights,” a panellist suggested.

Indeed, an audience poll during the Forum revealed that in fact, most organisations do not yet use the data that they collect in a coherent and intelligent way.

“A lot can be done in terms of understanding and utilising the data collected,” an expert explained. “Firstly, we must have a good grasp of the kinds of data we gather, and secondly we must hire the right people to decode that data into meaningful, useful information.”

“Let’s consider one aspect of wealth management that can be augmented by AI, namely return and risk,” another expert commented. “With every portfolio, a huge amount of information must be processed daily to filter out the potential risks. It would be prohibitive in terms of time, man-power and potential bias and error if human analysts went through all of the news from all sources each day, examined all of the fine details of the text and images and produced summary reports each day.” This can then be compared with AI that can accurately recognise entities, extract topics, generate data and screen all risk-related articles within a few seconds each day.

Joining the race

Adoption of AI and big data is accelerating, the panel agreed, with a large number of wealth management firms now setting out on this aspect of their digital journeys. “This is a key area where third-party data analytic companies can step in to help these wealth management firms offer more holistic insights for their customers, their portfolios and their risks, without the cost and potential pitfalls of trying to build this type of capability in-house and then getting it horribly wrong,” an expert observed.

And at the more innovative end of the spectrum, panellists reported that there are a few forward-thinking wealth management firms already efficiently using AI to analyse their internal customer data along with other external social, geographical, market and regulatory events to create sophisticated real-time risk calculations, thereby empowering their RMs and their customers to make better decisions.

But one of the stumbling blocks in harnessing the power of AI, a panel member explained, is that AI and big data are still nascent technologies, and nobody yet knows all the right questions to ask and therefore how to fully harness the potential that most surely lies ahead.

Consistent performance

The discussion then turned to how AI can deliver superior investment returns. “AI can do the heavy lifting, so with minimum man-power, we can try all possible combinations of factors, keywords, timeframes and methodologies to find the best fit,” a panellist explained. “In human-controlled portfolios, we see some diligent effort and the occasional home run, but there is a lack of reliability. With AI-augmented portfolios, we are seeing consistently higher returns.”

As well as performing consistently well, AI can drill down further than humans into subtle trends in the market. “Use AI to be greedy when others are fearful, and fearful when others are greedy, to adapt a Warren Buffett comment,” a panel member implored. “AI can use big data to decipher whether fear or greed is behind decisions and react accordingly.”

Are you in the mood?

An expert then explained that the investigation into and elucidation of investment trends are usually based on quite rigid fundamental and technical analysis but armed with AI exponents can now begin to recognise helpful nuances in the shorter-term sentiment, or ‘market mood’.

“This is only possible because we can now process unstructured data on social media platforms, blogs and comments on articles using machine learning to see what is driving market reactions” they elucidated. “No human can consume this vast quantity of information and accurately and dispassionately quantify greed or fear.”

The panel went on to clarify that because AI can do the heavy lifting, it helps free up humans to make better-informed decisions. “Hybridisation – AI combining with the human - can lead to deeper specialisation and greater capabilities,” a speaker explained.

Far from its full potential

Next, the panel examined the individual applications of AI, of which one of the most promising is natural language processing (NLP). Panellists said it can help wealth managers to better understand the market by deciphering and highlighting the main insights from many different sources in numerous languages.

However, although NLP can provide another strand of analysis to guide human decisions, an expert argued that NLP has not yet reached its potential. “If you came here today and told me there is an NLP system that can trawl through all the different reporting formats, in different languages, and make an intelligent recommendation that would not have to be analysed further by humans, I would be amazed. We are just not there yet,” he stated, “but the future of NLP is most certainly remarkably promising.”

Impediments to adoption

The discussion shifted to another hurdle standing in the way of a speedy uptake of AI. In real terms, in the near future, algorithms generated by AI will be able to remove human bias in decision-making, processing information based on preferences set out by the investor, which will, in turn, enable them to act more autonomously. However, the challenge then is for financial institutions first to trust the technology, and then to convince their customers to adopt and trust the output. “This is certainly not an easy sell,” an expert observed.

So, why do companies not seem to yet effectively use the data that they hold and that they collect? A panellist explained that there is just so much data, it is time-sensitive, and that most wealth managers did not know where to begin. But the experts then gave some practical advice for any organisation.

Data privacy is, understandably, one of the spanners in the works for data mining, and most jurisdictions are clearly tightening up on regulation and implementing strict data privacy rules. However, the younger generations seem happier to trade their data in order to receive a tailored experience with added personalised value, a trend seen developing on search engines, social media and entertainment platforms in recent years, and one that will no doubt be leveraged increasingly in the wealth management industry.

Money flows towards pain-points

The experts explained that while there are many types of AI, the three that the financial industry should currently be most interested in are firstly perceptive AI, which identifies and categorises data; secondly robotics AI, which focusses on process automation; and thirdly business AI, which is used to solve a specific business pain-point, for example building an algorithm to help with client prospecting. “Technology must fit with the customer’s needs, not the other way around,” implored one of the experts.

In the wealth management and private banking sectors there has recently been a lot of pain related to regulatory compliance and the raising of standards, the implementation of which has adversely affected customers and advisers alike, the experts agreed, leading to rising costs and decreasing productivity.

Data and AI can, however, be used to streamline client onboarding, leveraging the data in internal and external databases to make a profile on the client, their background, the source of their wealth. This information should then be used to make a standardised report, so each potential customer is comparable to others. This will also help to address the ongoing concerns of the regulators, say panellists, and enhance efficiencies and therefore help profitability.

Embrace AI

To wrap up the discussion, panellists were asked to consider their take-home messages to the audience. “Moving forward, there needs to be a general increase in technological literacy across the financial industry to understand the capabilities of AI, the right questions to ask and how to interpret and use that data,” an expert implored.

AI and big data analytics will lead to a much better customer experience as well as enabling wealth managers to increase their competitiveness, gain a better understanding of customer behaviour and preferences and reduce costs, the panel agreed.