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The objective of wealth management is to help clients plan for their financial future and provide peace of mind, all while keeping up with changing market dynamics and making the latest investment vehicles known and accessible.
Wealth management advisors use a consultative approach to learn what a customer’s goals and risk tolerance is, then create a portfolio of personalized products and services. Based on mutual agreement, the advisor will purchase the products for the customer, which entails asset under management fees, commissions, and various broker fees. Currently, human financial advisors earn, on average, between 1% and 2% of assets under management.
While this traditional approach seems straightforward, it is not sustainable or scalable. In fact, wealth managers report that they spend 60-70% of their time on non-advisory activities such manual data entry and paper processing. On the customer side, a Forbes study showed that 67% of high net worth individuals want their wealth manager to adopt AI immediately.
Much is at stake for the future of the wealth management industry. With an estimated $78 trillion of assets in motion worldwide available for capture, how can wealth managers find clients, provide the best returns, all while doing their jobs faster, cheaper, and with fewer steps and interactions with younger, tech-savvy clients?
This is where AI, data, and automation come in. Here are four areas where wealth management firms could use AI to their benefit and continue with the digital imperative.
- Personalization: AI models could be applied to client CRM data to understand an individual’s needs, attitudes, and preferences to create clusters that go beyond traditional wealth segments. They could bring in information from social media and other outside sources to present targeted information to clients when they log in to check their account balances. Understanding preferences around ESG, green initiatives, and sectors of interest can be used to suggest investment ideas, rebalancing options, and the construction of portfolios.
- Portfolio Management: How do you know when to buy or sell a stock or other asset? You know what a company’s P/E ratio and other quantitative measures are, but do you understand its exposure to upcoming litigation, customer satisfaction ratings, executive turnover, or seasonal trends? AI models can integrate qualitative factors into determining the likelihood that a company’s stock will fall without rising again and recommend if you should buy or sell an asset.
- Robo-Advisory: Did you know there are already over 100 robo advisors in 15 countries being used today? Early versions of robo advisors took client input information around factors such as liquidity and risk tolerance, and the AI models created single-product proposals. They now have become so sophisticated that they can automate asset shifts to rebalance portfolios. The popularity of robo advisors will continue to expand.
- Compliance Management: Personal finance organizations stay up-to-date on compliance regulations by discovering and reading public notices and investment policy statements, then preparing reports on the new information. This is very labor intensive, and technology’s first attempt at automation was with rules-based alert systems. Unfortunately, up to 90% of the alerts generated by these systems were false positives. AI is now being used to check the work of those ecosystems and reduce the false positives.
With those use cases as just the springboard, we believe AI’s role in wealth management will continue to grow in the areas of client relationship management, portfolio management, and back office operations, and that utilizing AI should be part of a near-term strategy.
If you are interested in learning more about how LRS can help you use all of your data to build AI applications, please contact us to request a meeting. Not there yet? We also offer strategic roadmapping services and can help you build an information architecture that will support your current and future analytical applications.
About the author
Steve Cavolick is a Senior Solution Architect with LRS IT Solutions. With over 20 years of experience in enterprise business analytics and information management, Steve is 100% focused on helping customers find value in their data to drive better business outcomes. Using technologies from best-of-breed vendors, he has created solutions for the retail, telco, manufacturing, distribution, financial services, gaming, and insurance industries.