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Personalization of Wealth Management in the Post-COVID World

Aamod Gokhale

Managing Principal, Business Consulting, APAC
Blog
  • Financial Services

The decade prior to the pandemic saw private banks take somewhat tentative steps toward adopting artificial intelligence (AI). This caution was born out of a natural skepticism at the very idea of “democratization of wealth management” and a belief that the client-advisor relationship was still the biggest factor that ensured client stickiness and business growth.

Then, COVID came along. The realities of the post-pandemic world have already started biting and have significantly accelerated earlier trends with the shift to digital interaction, expectation of lower fees and willingness to switch providers being examples among the six megatrends identified in a recent global survey of investors and wealth managers by ThoughtLab.

While deep client relationships are still important, in APAC the model is changing. As inter-generational wealth transfers start to occur, the younger scions of ultra-wealthy families often have a great affinity for digitization and prefer enhanced digital experiences. In addition, they may prefer to be connected to bankers around their age who are also digital natives, rather than with their parents’ bankers. For example, they can connect with the younger bankers via social media, chat applications and video calls, whereas their parents’ bankers may not be so digitally inclined. Intuitive digital experiences and innovative investment ideas are the top criteria for these tech-savvy investors.

However, the survey found that since the pandemic, this preference for intuitive digital experiences is not just limited to millennials or Gen-X investors but is now pervasive across all segments and all age groups. The older generation may not be as technologically-savvy, but due to restrictions in physical interactions, they have also started to learn to use various digital channels. It also found that democratization of wealth management is accelerating even faster in APAC. FinTech firms have started to offer wealth management products to the masses. Robo-advisors provide direct access to wealth products that were formerly available only to the ultra-rich. As a result, this puts a higher premium on even more “exclusive” offerings, such as personalized and targeted research recommendations.

Personalized Research: The Status Quo is No Longer Sustainable

Banks have traditionally produced research reports for their clients, hoping that the relevant insights would entice the client to trade products and buy services. The research is typically provided to clients for free, but these reports are usually generic and do not have premium advisory from the investment team. Premium advisories are personalized, and in the current state, wealth managers are unable to provide personalized research to each client due to scale, cost and complexity. This represents an area of the advisory practice that is primed for disruption, one that could benefit from cost and process optimization.

The onus of selecting and sharing relevant research is usually on the relationship managers (RMs). RMs provide recommendations on relevant publications to their clients since they are familiar with their clients’ investment profile and have market information at their fingertips. It is common for RMs to spend a few hours a day compiling personalized research content for their clients – which means less time spent on hunting for new clients and bringing new money to the bank, impacting RM productivity, and again highlighting the importance of process optimization.

This is no longer a viable business model. Fee compression pressures have increased and could impact revenue by a significant percentage in the long-term, so RM productivity must be increased continuously. RMs could delegate the research compilation work to their assistants or marketing associates, but curating and personalizing market data is no easy task. If the recommendations are not relevant or packaged appropriately, it can result in the opposite impact than intended – poor client impression and lower engagement.

Providing the right article at the right time improves overall client engagement, helping clients to realize the greatest value through making timely and important investment decisions. With the aid of AI, this process can be simplified for RMs. It’s akin to providing them a personal research analyst to help them make sound recommendations and judgements with regards to market fundamentals and technical data in real-time. 

The Role of AI and Machine Learning in Research

Wealth managers invest heavily in research teams because better research helps to improve client engagement, activity and stickiness. However, to achieve this, the research team cannot work in isolation. RMs may share client feedback sometimes, but often the research team has little visibility on the success and value of their insights due to the lack of data on readership sentiment and content feedback. An intelligent research tool can bridge the gap by allowing clients to rate articles, gathering readership data automatically, and capturing feedback from RMs and clients.

In addition, machine learning models can be used to gather insight about the client’s portfolio, behavior, sector interests, trading patterns and investment appetite. They can also take into account clients’ and RMs’ feedback on earlier articles and their reading habits. This is used to tune the recommendations to the most relevant and personalized research content for the client on a continuous basis.

Take, for example, a client who has equity holdings. Based on feedback generated by the machine learning model, they have shown a tendency to actively read articles on the automotive sector and related stocks. Leveraging this information, they will be sent sector-specific articles, as well as research on related sectors, such as semiconductors and microchips. If the client goes on to show interest in the stock price of tech companies, he or she will then receive recommendations for the tech sector from the research team. Such timely and specific research support can lead to better engagement with the RM and investment actions.

If a client shows interest in “reading more” on the sector, even the “further reading” list needs to be personalized for the client and should not be static. Such secondary recommendations can be managed directly by the AI and machine learning (ML) algorithms, which are capable of aggregating and scouring thousands of research articles in seconds and recommending the best related articles to each distinct client. This provides the client with a personalized service at a lower cost.

Continuous feedback loops from clients can be obtained via readership statistics, such as the number of articles downloaded, liked, read and shared, as well as related content (e.g., secondary recommendations) that the client is reading from the platform. RMs can provide further inputs based on their conversations with the clients on those topics. Research producers will be equipped with this new information to produce better research and can even put a premium on highly personalized content for selected clients.

There is one important area that must be considered for any AI based tool: data privacy. The data capture mechanisms and algorithms both need to be designed carefully to consider GDPR laws and relevant local data privacy regulations. They must allow clients to opt out, as well as building controls to ensure that data belonging to a client is used only based on client consent.

Key Takeaway: Accelerating Shift to Personalized Advisory

Personalized investment recommendations leveraging AI and ML will save RMs a significant amount of time by optimizing the curation process, time that can be better spent on engaging with clients and bringing in new money to improve the bottom line. Personalized research can also improve client engagement and increase client stickiness, helping to improve retention and justify the investments made in the research team with a more measurable ROI.

In the past, wealth managers relied upon in-person interactions and relationships to provide that personalized experience. The stark reality of the “new normal” where investors expect 75% of interactions to happen digitally, coupled with the expectation across age groups and customer segments of intuitive digital experiences, require wealth managers to adapt as quickly as possible.

Wealth management is still very transaction driven, but personalized research recommendations is a low-hanging fruit on the journey towards increasing share of the personalized advisory model.

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