Artificial Intelligence and Machine Learning: How to Scale Your Profitability Without Scaling Costs?

Darell Miller, Managing Director for APAC, was one of the panellists for Hubbis Digital Dialogue of June 20, in which the experts focused on the rising role of Artificial Intelligence (AI) and Machine Learning (ML) in generating refined data and insights that drive personalisation and, ultimately, client trust and therefore boost both revenues and scalability in wealth management.

Darell opened the discussion by observing how vital mined and refined data are for the wealth market’s well-acknowledged mission to enhance the capacity and capabilities of the relationship managers (RMs). If RMs are empowered with technology and the right processes, the theory is they not only have the resources and time to win more clients but also a greater share of wallet from existing clients.

“Helped greatly by machine learning, and then AI, you can drive personalisation for the clients and at the same time enable all your RMs to provide the same type of high-quality service and focus that most RMs today only deliver to their top 20% or maybe 30% of clients who produce the business,” he explained. “The banks and other wealth managers can thereby really scale their business, scale their profitability, but without scaling costs.”

Building on firm foundations

He elaborated on this, noting that ML and AI work ideally on solid data foundations that firms need to put in place. “That is crucial,” he stated. “Without the right data strategy and structure in the first place, ML and AI will not be effective, and you will not achieve the levels of personalisation and ultimately better service and scale that you seek.” Essentially, a single source of highly structured data is the foundation from which everything then flows.

Darell added that, in his view, AI and ML will not weaken the role of the RMs and advisors but would elevate and strengthen them. “It is all about enhancing insights, relevance and personalisation and augmenting the roles of the client-facing bankers,” he explained.

Take five

He elaborated on this, pointing to research on a variety of advantages that will flow from this approach. First, there is the automation of repetitive manual tasks such as risk assessment, portfolio rebalancing and data analysis, freeing up time for wealth managers to focus on complex personalised aspects.

Second, you can enhance decision-making using analytics and AI-driven algorithms to produce improved data-driven decisions based on the client’s personal financial goals and risk tolerance.

Third, AI can help improve efficiencies and cost-effectiveness. Four, it will help achieve more tailored and personalised experiences at scale for clients, allowing advisors to better understand their preferences and needs.

And five, he concluded, AI support better risk management and regulatory compliance by analysing the vast amounts of data and identifying underlying potential risks and irregularities.

Keeping things personal

But he stressed that wealth management will continue to rely heavily on human interaction, trust, empathy, and emotional intelligence, and that AI and ML will be enhancing the individual approach. “It is all about doing more with the technology available,” he said. “Yes, there will be some concerns about the impact, but the businesses and people who embrace it and take it forward will get the most out of it.”

Darell then refined some of these observations, noting that you can have all the augmented and refined data in the world, but if you do not map and adjust your workflows to the new reality, the end clients will not ultimately see any benefits. “You must have the right approach so that you can really add value with these solutions,” he advised.

KYC at warp speed

Darell shifted his focus to the use of data for onboarding and compliance, noting that if every single team has access to the same high-quality data, which is easily accessible internally, the onboarding times can be shortened potentially from months to a matter of days.

“We know of several banks that have transformed their onboarding processes from around 45 days on average to a single day!”, he reported. “And incredibly, we learnt of a customer bank that had onboarded an offshore client and his wife in less than three hours, start to finish, completing all checks and documentation.

Have faith

Darell concluded his observations by reiterating his belief that in the wealth management industry, AI and ML will be empowering forces, and not destructive to the essentially personal approach of the past. He explained that it is about enhancing the individuals concerned, not removing them from the equation.

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