In the ever-changing environment of wealth management, the value that Artificial Intelligence (AI) in all its guises can provide is increasingly recognised. Although widespread adoption has yet to happen, more and more firms are taking an interest and actively looking to explore ways an AI model could be successfully applied to their architectures and define the benefits of doing so.
Indeed, existing use cases tend to be in areas that involve handling lots of data or following set processes – anything where inference is not required, and there is a set answer to a set question. The aim is scale and efficiency. This data processing, form filling, and chatbots able to answer clearly defined questions are all already in play.
But as AI matures and breaks out into various subsets, the range of functions it can fulfil expands. AI now enhances the service proposition in any number of areas: prospecting, onboarding, personalisation, and sentiment analysis. The most significant element is thought to be around improving the client service from every angle.
The timing could not be better!
Expectations regarding the level of service and its delivery at the front end are high. Customers are used to Netflix or Amazon-like experiences in other areas of life and now largely expect the same from their wealth manager. Establishing meaningful connections with clients by exceeding expectations is the key to long-term success. To ‘exceed’ the provision of a broader range of services and hyper-personalised financial guidance underpinned by flawless user experience is required. And that means embracing AI.
However, that is not to say that the importance of the personal touch and an engaging and trusted relationship has diminished. Far from it, clients still want this, so the conundrum is how to supercharge the adviser to provide better insight and service. This is where AI, in its various forms, can help.
Automation and process efficiency
AI can indirectly boost the adviser by giving them back time. Indeed, AI makes an adviser far more effective because it can underpin automation, particularly when it comes to form filling, creating notes, and next-best-action prompts. By extracting data from a variety of sources and funnelling it in a meaningful format into the adviser’s customer relationship management system, advisers spend less time on form filling so that they can spend more time with clients and make their conversations meaningful by having information at their fingertips.
Personalisation
Personalisation techniques can further enhance the delivery of information. AI can analyse several disparate data sets to come up with insight on what a given individual will find useful at a distinct point in time: someone looking to sell a business, someone looking to invest in a specific asset, someone looking for succession planning, etc. By identifying each client’s unique circumstances and likely needs, the adviser gains the ability to enhance and deepen the relationship by providing timely and relevant insights, information and contacts. In the same vein, AI can perform client segmentation much more accurately than a human ever could and, thus, enhance marketing and prospecting efforts.
In short, AI platforms allow wealth management firms to get a much deeper insight into customer and market data, enabling substantially more effective decision-making and a proactive approach that is likely to yield positive results.
Creating tailored information
Related to sharing the right information at the right time and automation is the ability to create entirely new content. Indeed, Generative AI is the latest form of AI, and depending on who you believe, is set to revolutionise the world. It works by taking in several sources of information, analysing them and coming up with new output – as opposed to just coming up with an analysis of existing information. This could be a written piece, data analysis, imaging, video, or more, such as creating personalised investment plans, providing information about specialist investment opportunities, etc. The potential of Generative AI in the knowledge economy is huge because it can work closely with humans for an end result that would have taken much longer without this new technology.
Sentiment analysis
AI is another use case to accurately predict customer behaviour and conduct sentiment analysis. A key benefit is identifying if someone is worried or unhappy and ultimately giving the wealth managers an early warning, reducing client churn – something all wealth managers want to guard against.
Risks – explainability
Indeed, the potential benefits of AI at the front end are vast. However, care needs to be taken to mitigate and manage risks while the regulatory landscape evolves and develops to catch up with AI’s application. Without this, the industry will shy away from widespread adoption and even see AI as a double-edged sword, where the risks of getting something wrong potentially outweigh the benefits of the technology.
In particular, the explainability of any AI algorithm needs to come under the spotlight, be managed, and monitored. Thus, models need to be transparent and provide comprehensible decision paths for their predictions. And the insights gained through AI must be clearly explained by advisers to clients. In addition, when using AI in an advisory sense, comprehensive risk management needs to be in place in order to mitigate against the risks of falsification, AI bias, and AI hallucinations.
This gains even more importance in the context of GDPR and the ‘right to disclosure’. People have the right to know how their data is used and for what purpose. Transparency and accountability are, therefore, indispensable elements of AI integration in financial services.
Data
The other big barrier to uptake lies in data management. If users cannot trust the accuracy of the data being used in an algorithm, then it follows that the output, too, is untrustworthy. Accurate data, however, makes for a rich data output that wealth managers can rely on to make decisions and inform processes. Larger companies generally have much better access to data science and data infrastructure and a much larger pool of resources, so it is here where we might see adoption rates pick up first.
Conclusion
Ultimately, AI in all its guises is still a work in progress and, like other technologies, can work to underpin and enhance the adviser but not replace them. Indeed, the wealth management community still relies on providing a nuanced understanding of individual circumstances, emotions and long-term goals for success. Algorithms cannot ever fully replicate humans, and so the role of AI is to provide more of an enhancement effect, as opposed to a replacement one. Its use lies in creating capacity for humans, thus augmenting human expertise for the benefit of clients.
