AI in Wealth Management: Challenges and Opportunities
Artificial Intelligence has dominated headlines across industries throughout 2023, but the big question that remains on everyone’s mind is, “How will it impact me?”. From predictive analytics to personalised investment strategies, the world of wealth management is embracing AI to reduce risk, increase efficiency, enhance customer experience, and ultimately improve margins.
We spoke to Kurt Vanhee, Managing Director of Continental Europe & North America at Objectway, to gain his insights into the challenges and opportunities of AI in wealth management, which he ended with a key point to reflect on.
Objectway is a partner in the digital transformation of private banks, wealth and asset management firms, so you can undoubtedly testify to the financial sector's evolution towards more transparency and customer-centricity.
How do you balance that with the massive investments in regulation?
We believe that organisations should look at regulation and compliance as an opportunity instead of a pure obligation. It is a chance for the business to review the client relationship in an effective way. The common misconception that compliance is just a legal requirement, an annoying box to tick, is something that should change. A sound compliance system can help the business achieve better customer engagement, improved customer retention, and a longer retention period for the client.
In that respect, transparency, customer centricity and regulation go hand-in-hand.
The average company perceived GDPR, for example, primarily as a compliance exercise, but GDPR mandates that all information about the customer is documented, and so far, that has not always been the case. So more than an exercise, compliance is also a business opportunity: this is a perfect occasion to review customer insights in the workspace, to make sure to map all the data required and reflect on the business value of the additional insights that are possible. That way, you map critical data for both your compliance department and your sales organisation.
How can technology support this while keeping transformation costs under control?
First, organisations must reframe compliance and regulation as a business enabler. Once you have reframed the mindset, it is easier to build a compelling business case for your transformation.
Banks, wealth managers and asset managers manage huge volumes of complex and increasingly detailed data. Digital transformation and automation programs can help to reduce the cost of compliance while improving data integrity. That will lead to more valuable insights, both from a customer insights point of view and from a risk point of view.
We believe that these improvements are only possible with smart AI usage. Success will only be possible with quality data and by keeping the end goal in mind from the very beginning, because artificial intelligence is not a solution to everything. Identify what you want to achieve and scan the market for the right vendor that can help you with your specific business case. Together you frame the scope and the problem to solve, and you select the proper tooling based on the data available in your organization.
What's the current state of AI adoption in the sector? And where do you see the most uptake of AI technology in wealth management?
It is still early days for AI in wealth management. However, considering the investments made so far in this area, we truly believe there will be a substantial impact, especially if we compare these investments with previous technology adoption cycles. Today, we don't see large-scale of adoption, but the impact of AI will be critical in the longer term.
The integration of artificial intelligence in the financial sector will certainly further enhance the role of the advisor. The critical question is: how can you simplify their work in a number of areas? Supporting the adviser with additional insights will be pivotal, and that's where AI will play a role: process automation, personalisation of the relationship, and detecting the perfect time to contact the client, all with a data-driven approach.
We have been using AI to enable our clients to develop better-performing investment strategies. Imagine the advisor suggesting an investment proposal to a client. AI can optimise the model portfolio that the client is connected to. Approaching this in a data-driven architecture leads to a highly personalised portfolio based on a better and more holistic view of the client, ensuring a more precise offer aligned with a client’s wishes and suitability assessment. Combined with a high level of automation of back-office processes, the financial firm can reduce the cost of managing the client relationship in parallel, which leads to higher margins.
Other areas include client onboarding and risk assessment, where again AI has enhanced the role of the advisor who is able to give better advice, with a better understanding of the contextual risks.
Together with the University of Bari, we developed what we call a “customer churn rate prediction system”. Based on vast volumes of both internal and external data, the system detects patterns and predicts when a client is at risk of leaving the firm. Needless to say, these kinds of predictions are of enormous value in wealth management.
If not used responsibly, AI comes with risks, especially with the outputs not being explainable. How is the industry answering these challenges?
Processing large amounts of data is critical these days. More data is being processed every day, and it's more complex data. AI can analyse large data sets, identify patterns and generate insights at a scale and speed that is far superior to humans. So, productivity gains and improved customer experience are the key drivers. What is vital in developing these things is transparency and explainability. You can build the best predictions and the most beautiful dashboards, but you still need to deliver transparency, not only internally but also externally. Transparency and explainability are not just a matter of managerial responsibility, but also required from a GDPR point of view.
I recently spoke to a CIO in the wealth management space and he explained to me that generative AI is just an evolution of many years of new AI developments. We shouldn't look at it as being a disruptive technology. What's your take on that statement?
My view is that we should embrace successful adoption, not disruption. If you disrupt something, you do not necessarily know what the result will be. You disrupt, but you don't know if it will be to your advantage or not. So, successful adoption is all about what we said at the beginning: prioritising the right use cases for a wealth manager.
The target is not implementing AI. The target is the problem you wish to solve.
What is the right use case? Where do I want to put my money on? Identify the business drivers and scan the market properly to find the right technology partners who can work with you.
So, it's not about introducing new technology. It's about how and where I will use it.