AI and the dawn of hyper-personalisation in wealth management

The application of deep-learning models will enable wealth managers to tailor their offerings to suit high-net-worth individuals’ exact needs and goals

Artificial intelligence stands to reshape the human experience, and the wealth management industry is no exception. According to PwC’s 2023 Global Asset and Wealth Management Survey, assets managed by algorithm-driven and increasingly AI-enabled digital platforms will surge to almost USD6 trillion by 2027, nearly double the figure for 2022.

But as AI begins to make itself felt in the industry, high-net-worth individuals (HNWIs) are emerging as some of the biggest beneficiaries – not only in terms of their demands for the best investment advice but also for their expectations of a highly personalised service. “AI will enable wealth managers to know their clients better by analysing a wide range of datasets and generating insights that were previously difficult to obtain,” says Demir Avigdor, Head of Private Banking UK, Standard Chartered.

AI will enable wealth managers to know their clients better by analysing a wide range of datasets and generating insights that were previously difficult to obtain

Demir Avigdor, Head of Private Banking UK, Standard Chartered.

Financial advisors are in little doubt that AI will mark a “before and after” moment for the industry: a 2022 survey by Accenture showed that more than eight out of 10 financial advisors believe that AI’s greatest benefit is translating the clients’ data into actionable insight for their benefit.

A revolution in financial forecasting? 

In a world marked by increasing geopolitical and market volatility, one transformative application of AI is in financial forecasting. Deep-learning models can incorporate thousands of data points from a wide range of sources, which will improve accuracy and increase the likelihood of better returns over time.

Market sentiment analysis will be capable of including more nuanced sentiment from news outlets and social media platforms. Weather patterns and forecasts will be able to inform predictions about agricultural commodities as well as the effects of extreme weather events on markets. AI will also be able to factor in public opinion on a wide range of issues that can affect everything from currencies to the price of sugar.

“Legacy and traditional forecasting approaches strain to synthesise inputs rapidly enough to drive agile decision making,” says Chase Hughes, CEO of ProAI, an AI-powered platform helping businesses start, fund and scale. “When applied thoughtfully, AI financial forecasting methods can ingest disparate data sources, identify non-obvious correlations, adapt models dynamically and generate forecasts exponentially faster.”

“Generating alpha has always involved rigorous analysis of fundamental and technical indicators to identify patterns which can be used to predict opportunities and risks,” the CFA Institute said in a 2024 article. “The difference with machine-learning algorithms is that investment managers can now reach much deeper into granular data to identify complex patterns that traditional methods might overlook.”

A new era of hyper-personalisation

In addition, AI is leading to a new dawn of hyper-personalisation in the industry. One of the challenges wealth management faces is processing and making sense of reams of the client-specific data now at the disposal of clients and managers. Much of this data – investment portfolios, tax information, estate planning and insurance plans and updates – are spread across different platforms, which makes access cumbersome.

AI models will be able to handle and mesh these siloed datasets with ease, as well as discern patterns and opportunities within them and across them. They will then be able to tailor all of that insight to fit the specific needs and goals of the individual. “Forecasts can be finely tuned to reflect the unique circumstances and preferences of individuals across different demographic groups, and can provide actionable insights that traditional models might miss,” says Avigdor of Standard Chartered.

Forecasts can be finely tuned to reflect the unique circumstances and preferences of individuals across different demographic groups, and can provide actionable insights that traditional models might miss

Demir Avigdor, Head of Private Banking UK, Standard Chartered.

For a member of Generation X, for example, AI might notice that she has increased spending on healthcare and wellness apps, indicating a growing focus on health. Coupled with data on her putative children’s educational expenses, AI could forecast a shift in her investment strategy towards more conservative options as she balances health, family and retirement needs.

Generative AI will allow clients to ask specific questions about their portfolios, and use simple language to help a dual team comprising AI and a human wealth manager to tailor products and investment advice to suit a client’s specific needs.

More communication, better-quality communication

A third transformative area for AI is its ability to improve engagement, providing clients with a host of self-service models that can give them easier access to their portfolios and provide deeper insights into expected market conditions and scenario planning.

HNWIs and UHNWIs need and demand a seamless service wherever they are and at any hour of the day. AI tools will be able to bridge the gap that can potentially exist between clients’ excellent experience during office hours and their expectation of service out of hours.

As part of that deeper engagement, AI can even help HNWIs with acquiring more and better educational information than previously available, providing sophisticated resources for finessing financial literacy and building client knowledge. “AI can greatly enhance financial literacy by offering personalised, interactive and accessible educational experiences,” says Avigdor. “By tailoring content to individual needs, providing real-time feedback, and proactively addressing knowledge gaps, AI could empower clients to make informed financial decisions.”

Ultimately, argues Avigdor, AI’s biggest benefit from a client-service perspective is its ability to automate a wide range of tasks, leaving wealth managers with more time to spend talking to and meeting with clients. Generative AI will automate portfolio rebalancing based on pre-set criteria, ensuring that portfolios remain aligned with the client’s goals without manual intervention. AI-powered virtual assistants will be able to handle routine client inquiries and transactions, freeing up wealth managers to focus on more complex and high-value interactions as well as personal interactions.

“Sensible use of generative AI will enhance the client experience in wealth management by providing personalised, real-time insights, proactive assistance, and improved communication,” he says. “In the future, wealth managers will spend much more time focusing on strategic advisory roles and deepening client relationships.”

This content was paid for by Standard Chartered and produced in partnership with the Financial Times Commercial department