Wealth managers in the Asia-Pacific region face data, governance and employee-engagement challenges when adopting AI-driven personalization. If they can avoid these roadblocks, the potential rewards are considerable.

It is likely that all businesses involved in the wealth management sector may ultimately need to invest in an artificial intelligence capability if they want to be competitive in the Asia Pacific (APAC) region going forward. Leveraging AI could bring two invaluable qualities to wealth managers (WM): relevance and engagement. Human relationship managers (RMs) won’t likely be able to stay on top of their client outreach unless they have AI-driven apps that can help direct the right information to the right clients at the right moment, as I explained in my previous blog.

AI could help RMs better understand their clients’ needs. They can notice e.g. whether clients are browsing the firm’s microsites for education- or travel-related needs, and then can use that data to generate leads. AI can help generate recommendations from news, for instance the portfolio implications of an election result elsewhere or from an economic issue trending on social media. Most importantly, AI could allow for personalized recommendations to be sent out at scale–which is crucial, in a market as large and fast-growing as APAC’s.

The way forward with AI

In my view, there are by and large two ways of getting AI development right. One is to take a simple route, such as rules-based logic. This would allow wealth managers looking to implement the technology to go live relatively quickly. For creating an advanced technology solution, however, a WM is likely to have to build new capabilities from the ground up. This means sourcing clean data, opting for the right operating model, and then piloting it with a small group of relationship managers before scaling to the rest of the firm. This is usually a multi-year journey.

Which approach to take mostly depends on the size and available resources of the institution in question. The very largest WM are the most likely to take on the development themselves, while smaller institutions in APAC might in many cases be partnering with third-party technology companies in order to bring AI solutions to market. In either case, firms undertaking the AI journey face three main roadblocks: foundational data capabilities, governance and risk management, and employee adoption.

Safety, compliance and engagement challenges

AI needs data, and data privacy is fundamental when it comes to wealth management. WMs need to consider carefully how to protect the information of their high-net-worth clients from internal and external threats. This has become all the more pressing given the sudden shift to remote-working caused by COVID-19, which at times has created disrupted controls and isolated staff.

Governance and risk-management pose a similar challenge. AI-driven applications must comply with laws such as CCPA and GDPR, but the regulatory situation becomes even more complex in APAC, given the absence of any continentally dominant standard-setter equivalent to the US government or European Union. Instead, WM firms operating across the entire region need to align with market-specific laws that might not always be clear or yet fully developed.

All recommendations, including those generated by AI, carry the risk that a client could lose money by following them. As a result, governance and risk-management need to be transparent, with AI applied to the intelligent automation of analytics, rather than as a machine-learning exercise in which an algorithm analyses historical data and acts on it without explicit programming. That approach could be fraught with risk.

Employee adoption poses a third challenge, because AI uptake requires a new, “hybrid” way of working that combines human relationship management with the AI-driven dissemination of recommendations and research. This means carefully planned employee engagement and training. One advantage that firms in APAC may have is that relationship managers in the region tend to be younger, with a relatively high technology quotient that would allow them to adapt rapidly to system-change.

Where to start?

Accenture’s latest research on AI in Wealth Management indicates that while most WMs recognize the value of AI-based business transformation (and some 84% agree AI could transform the industry in the next five years), many have encountered challenges when it comes to implementation. There are however some “no regret” moves firms could take if they haven’t already. One is simply to perform an honest assessment of where their progress stands compared to that of the industry as a whole, in terms of vision and value achieved so far. Firms should consider what they seek to accomplish, and how their strategy aims to bring those goals to fruition.

Then, they should rationalize and adopt a fast-fail mentality to AI; in other words, try various things in order to quickly understand what works and what doesn’t. For any AI use-cases in development or at proof-of-concept stage, push to develop in a pilot environment or full-scale production so that the business results can be measured across APAC markets. And for areas in production, build and strengthen multidisciplinary teams, partnering across business and operations to transform surrounding processes and encourage forms of adoption that amplify results.

By taking these steps, APAC WMs could ensure they’re taking advantage of the revenue-generating potential that AI-powered personalization offers. If you want to find out more about what it takes to put this into action, please do get in touch with me directly.