At its purest level, the interaction between a wealth manager and their client is a series of conversation where the client exchanges trust and the manager exchanges advice. If that was all there was to wealth management, then life would be easy for both parties.
However, there is compensation for the manager’s experience and time, there are regulations to adhere to, research to conduct, due diligence and so on which usually lead to both parties being unhappy: the advisor doesn’t have time to do what they do and love best and the client feels the advisor doesn’t have enough time for them.
Looking at the Cap Gemini’s 2023 World Wealth Report, it breaks down the none-core and core activities that an advisor spends time on. In short, two thirds of the advisor’s time is spent on non-core, which prevents them from doing the job they love and unnecessarily creates client discontent.
Given that AI is being touted as the solution to all, I thought I would look to see how AI could be used in the above activities to reduce friction. In Part 1 of this topic, I will look at what could be done by wealth firms to reduce the time spent by wealth managers on non-core activities if they were to incorporate AI. In short, although there are some ways AI can help, I believe there are more fundamental issues around not utilising existing automations correctly, either because of poor UX and badly designed workflows which create friction in the non-core activities.
This is an area where AI has a chance to make a big impact. Information and subject matter know-how are key to anyone excelling at their job. However, for wealth managers, there is so much information to know about the markets and all the various products, that it becomes a full-time job in itself.
The challenge for wealth firms, is how to assemble all the required information and disseminate that information in a succinct manner to wealth managers. This is where NLP and text summarization models can be utilised to not only summarise news daily so that it only takes 60 seconds or less to catch up with what’s going on, but also do the same with internally or externally generated research reports.
Chatbots can also be connected to different research allowing wealth managers to get answers to prompts (pre-existing or generated) or have the model automatically generate summarized research ideas based on a client’s portfolio.
I would argue that although this seems to be the activity with the least amount of time spent by wealth managers in the non-core section, the solution is simple and the impact would be great on the advisor – client relationship.
Automation should have already solved for reducing friction around trading execution so the fact that wealth managers are spending 8% of their time on this, suggests it is an issue around the administration, UX and workflow, rather than something that AI can solve for.
That said, AI can help automate order placement and monitor historical market conditions to identify optimal execution times.
Again, automation with streamlined application forms, UX and workflow should have already solved for reducing friction in this area. Furthermore, there are already simplified credit approval software which can connect to a user’s bank account and assess the cashflows to determine what they can afford and what risk they pose.
Wealth firms could use AI to enhance that application process to improve the process of assessing a borrower’s history by combining the cash flow history with other proprietary data sets such as types of investments and risk profile, notes from an internal CRM (e.g. job status, levels of engagement), social media profiles etc, but that’s more a benefit to underwriters rather than the front line wealth managers.
Poor UX and workflow seems to be a running theme where a lot of the friction experienced in these activities should have been reduced already by automation.
Nevertheless, with tax and compliance, AI could be used to enhance those automations by monitoring regulatory changes and identify compliance risks by incorporating some of the same data mentioned above that could be used to enhance the process of making loans. However, I would see this more as a benefit to the back office than the front office.
AI can make a big impact in smoothing the onboarding process by speeding up and automating the information gathering process, and using many data sources to ensure there are no compliance risks. There are many solutions in play where AI is used for faster identity verification. However, there are other use cases too.
For example, AI-powered chatbots could be used to gather client information such as their goals and attitude to risk, answer questions when the user requires clarification or ask questions when the information seems incomplete.
Similarly, as for the process outlined in Loans and Tax and compliance, AI can be used to combine many data sources to get a better picture of any potential compliance risks.
There is a good opportunity with AI to improve the time spent on these non-core activities, however, it could be somewhat controversial as it either revolves around being more big brother or cutting out the noise and streamlining information.
For example, to help HR monitor employee performance, internal data can be combined to assess productivity, engagement on internal systems, internal communication tools. Less controversial, an AI powered chatbot could be trained on all company related matters and improve HR service levels.
Although AI can be used to personalize training, it’s more important function, in my opinion, would be to streamline the training and information using NLP – in a similar manner to the disseminating market and product information.
In a perfect world, wealth managers should be spending the vast majority of their time on activities which improve the client relationship. Ideally, automation should have reduced some of these non-core activity blockers already. However, the Cap Gemini report suggests that firms aren’t even embracing technology that has been around for over a decade, let alone benefiting from what AI could do. That said, there is no time like the present, and if AI can help turbo-charge the digital transformation process, then all the better.
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