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Exploring AI trends in wealth management

Patrick Donaldson
Patrick Donaldson
Head of Market Development, Wealth Management – Asia Pacific & Japan

Wealth managers are increasingly deploying artificial intelligence (AI) to meet the needs of a new generation of tech-savvy high net worth individuals. Our recent round table event highlighted some of the AI trends in wealth management in 2019, including the importance of quality data and the use of market sentiment as a potential leading indicator.


  1. Younger, tech-savvy high net worth individuals are shaping the trends in wealth management in 2019, driven by their preference for digital touch-points.
  2. Wealth management firms can use AI to help deliver a richer client experience, as well as to enable productivity within wealth managers when performing certain higher value, strategic tasks.
  3. A recent Refinitiv round table highlighting some key AI trends in wealth management considered challenges around data quality and supply.

The next decade will see a surge in demand for wealth management services, driven by a rise in the number of new high net worth individuals (HNWIs) and the significant amount of wealth that will pass from one generation to the next.

According to the 2018 Asia-Pacific Wealth Report (APWR) released by Capgemini, the APAC region saw a 12.1 percent growth in HNWI population in 2017, and a 14.8 percent rise in wealth, with the region now forecast to surpass US$42 trillion by 2025.

While globally there is no definitive figure for the amount of wealth that will be transferred during this timeframe, research by Asian Private Banker suggests that within Asia this could be up to $9.5 trillion.

A core consideration for wealth managers are the preferences of a younger generation of HNWIs who are increasingly tech-savvy and commonly favor digital touch-points over conventional ‘in-person’ client servicing.

With access to growing and unprecedented volumes of data, firms have the opportunity to deploy artificial intelligence (AI) to deliver a rich client experience, as well as to enable productivity within wealth managers when performing certain higher value, strategic tasks.

While full of positives, this shift also presents serious challenges. Below are ten AI trends shaping wealth management, as identified at the recent Refinitiv round table, Applying AI to Wealth, which was attended by representatives from some of the top private banks and retail brokers in Asia

Data quality is biggest ML challenge. Exploring AI trends in wealth management

1. Quality data is a necessity, but in short supply

AI applications are only as good as the data they use. The quality of data held by wealth managers is not optimal, nor does it reside in a single repository. These two impediments are holding back AI’s potential.

The solution is to instil systematic policies for capturing, storing and managing data across firms. Wealth managers should also consider working with third-party, specialist solution providers to assist them with the implementation of these policies, as well as the day-to-day management and analysis of datasets.

2. Co-mingling of proprietary and external data essential

To maximize the potential of AI, wealth managers must co-mingle their proprietary data with that of external sources. While this continues to cause quality, technological and privacy challenges, among others, the insights that emanate from this process can be ground-breaking.

To understand the impact that emanates from a news event, for example, firms must map alternative sources of data to a client portfolio. Only by bringing together all of this information can managers unveil the true opportunities and risks presented by such an event.

Patrick Donaldson Quote. Exploring AI trends in wealth management

3. Increasingly AI is being applied to new tasks

One of the AI trends in wealth management is the potential for the technology to move beyond conventional tasks such as KYC and risk management, to new means of enhancing relationship management and client experience.

On one hand, firms are exploring how they can make their relationship managers more productive. On the other, there is a new generation of clients who only want predominantly online services, prompting banks to examine how they can optimize their digital offering.

Watch video: Applying AI to Wealth Management with MarketPsych Indices

4. AI can be applied to sentiment to create investment insights

How people feel about an asset predicts whether they buy or sell. Commonly there is a time delay between an event and a movement in asset price, as people take time to assess their positions.

Using AI, managers can track in real-time the sentiment of the market, and try to predict the movement of tradeable assets to provide sophisticated advice to their clients.

As an example, MarketPsych Indices provides easy-to-interpret real-time analytics across news and social media by continuously scouring more than 2,000 financial news sources, and more than 800 blogs, stock message sites and social media platforms, to assess market perception across stocks, bonds, commodities, countries, currencies and cryptocurrencies.

These AI-powered indices present wealth managers with real-time insights on how an asset may soon perform, and attempt to predict whether this will result in a positive or negative outcome.

5. AI-driven investment to outperform conventional practices

According to MarketPsych, the top 100 stocks measured by positive sentiment grew 14-fold between 1998 and 2018. This is about seven times greater than the performance of the S&P500 over the same timeframe. This performance isn’t limited to the U.S. markets, with major exchanges around the world seeing similar trends during the two decades.

Equipping wealth managers with these insights should increase the value that they can provide to their clients, by being able to more easily form an opinion about an investment idea.

U.S. stock market sentiment vs S&P 500. Exploring AI trends in wealth management

6. More clearly explained

The difficulty in explaining AI is challenging to managers and their clients. Both want to know how a recommendation is arrived at, and increasingly are cynical about information that has been ‘machine’ created.

Wealth Managers will need to be able to explain to their clients how they arrived at a particular conclusion. Likewise, when a manager doesn’t agree with a machine, data scientists must articulate how a result was reached in a transparent and easy-to-understand way.

7. Front office and back office to collaborate

Most AI programs continue to be run by back office technology teams. This can create a disconnect with the front office. Wealth Managers may be concerned that that AI-generated ideas don’t match the needs of clients, due to a lack of input sought from the front office.

Relationship Managers simply aren’t motivated to capture datasets. The solution is to incentivize the front office to collect new data, as well as collaborate with colleagues who develop AI-powered products and services. The result should drive productivity for Relationship Managers and an improved experience for their end clients.

8. A holistic approach to regulation required

Regulation governing the application of AI in wealth management is either mixed or non-existent in Asia. Managers have concerns over accountability and social responsibility, and may be uneasy with decisions being made without some level of human involvement.

Cybersecurity challenges are a major obstacle within many firms, and issues surrounding encryption and the misuse of personal information are also concerns.

While there is no single silver bullet to solve the problems, continued dialog with regulators, and the setting of robust security and data privacy policies will alleviate many of today’s challenges.

9. Could market manipulation be on the rise?

While AI is being used to positively support the needs of managers and their clients, could there be a danger that it is also being deployed to manipulate markets? Through the use of bots, perpetrators are able to influence market sentiment and encourage investors to buy or sell.

Advanced AI-powered monitoring tools, however, are able to recognize these activities, and remove them from their scouring activities.

10. Human involvement remains critical

Despite the significant technological advancements made over the past half-decade, humans remain critical to the development, management and oversight of AI. The utopian vision of machines autonomously providing wealth management services, therefore, is unrealistic.

AI has the ability to transform the client experience in wealth management firms, as well as enabling wealth managers to better perform higher value, strategic tasks. To get there, wealth managers need to focus on the quality of their data and work in close collaboration with the back office teams to get the most value out of it.

To learn more about how wealth managers can leverage AI, listen to this on-demand webcast with Celent: Putting the AI in Data.