To what extent have wealth managers embraced AI?

Adoption of AI technology has come a long way in recent years, especially in the back and middle office of financial institutions where the ability of AI to sort vast quantities of data, automate repetitive processes and accurately identify outliers is particularly valuable. From anti-money laundering to payment fraud prevention, AI is helping financial institutions to not only streamline their workflows, but to increase service accuracy by minimizing the risk of human error.

Now, we are seeing increased AI adoption in the front and investment office, too, where wealth managers have traditionally been reluctant to incorporate AI in the advisory process. However, when looking into the attitudes of wealth management clients, it is clear that the time is right for AI to play a greater role in areas such as portfolio analysis or optimization. According to a survey we conducted last year among affluent to UHNW investors, 77% of respondents would feel comfortable having AI support or fully lead the analysis of their portfolio data, 73% are open to receiving investment advice supported by AI, and 74% are willing to receive AI-assisted product recommendations. However, we believe it will be some time yet before we see wealth managers roll out a fully end-to-end AI-augmented investment advisory process.

What are the current use cases of AI in the front office?

The main benefits of AI technology in finance are enhanced operational efficiency, increased relationship manager productivity and improved data analysis. With these benefits in mind, we have identified two key areas where AI is already established in the wealth management sector.

The first use case is virtual assistant technology to augment the role of the relationship manager. Smart virtual assistants can support relationship managers, for example, by providing near-instant suggestions to client requests for account statements, transfers, trade proposals, etc. This works based on natural language processing (NLP) – similar to the technology behind ChatGPT. The virtual assistant analyses client-adviser communications, understands the client’s intent and suggests next best actions or relevant news items for the adviser to share. This AI support enables the relationship managers to serve a larger and more diverse client base, while ensuring quicker responses to keep clients engaged.

The second is improved client lifecycle management. Wealth managers can deploy network analytics to automate prospect mapping, while churn prediction engines can alert relationship managers when a client is at risk of leaving the firm. In our experience, these tools can enable wealth managers to increase their client acquisition rate by up to 20% while staving off client attrition.

What emerging use cases should we watch out for?

Advances in NLP will help drive conversational banking – i.e. interaction with relationship managers over multiple channels – and substantially increase the productivity of bank employees. An exciting future application of this technology is the combination of NLP with a voice-to-text solution. This would enable the AI to suggest next best actions in near real time, such as generating trade ideas, to the relationship manager during client meetings or generate a summary and to-do list after the meeting has finished.

Where do you see the future of AI in the banking industry?

The biggest change on the horizon is new regulation on the use and ethics of AI. A prime example is the pending AI Act by the EU Commission, which sets out clear guidance with respect to fairness, verifiability and non-discrimination. We believe that this guidance will give decision-makers the regulatory confidence to implement value-adding AI tools to leverage their vast datasets, which will ultimately boost innovation in the financial sector. We also expect increased client acceptance of AI over time, especially as individuals become more familiar with AI-augmented tools such as ChatGPT.