Jaco Cebula, Chief Technology Officer of Multrees Investor Services, explains how artificial intelligence (AI) is shaping wealth firms’ internal processes and client experiences.
With potential for increased efficiency and reduced costs, the application of AI in the wealth management industry is set to escalate rapidly.
AI can assist wealth management firms in two broad areas: ‘first line’ client facing interaction, and in the automation of internally facing processes and capture of knowledge.
The aim is for AI to improve the experience for clients, whilst also enhancing a firm’s internal processes and efficiencies.
In a client facing capacity, AI can provide an option for the first point of digital contact, with the ultimate intention of saving both the firm and the client time for routine tasks.
It can also prompt strategic interaction based on critical ‘events’ that are derived from the full extent of personal and financial data available.
However, a key consideration is ensuring that any AI solution, such as an adviser chat bot, can recognise when it is on the verge of exceeding its capabilities and should pass the client to a ‘real’ adviser – getting this hybrid model correct is a key challenge in this market segment.
An alternative, and possibly more innovative approach, is to integrate facial and voice emotion processing APIs into your apps that can be used to identify when the client experience is becoming strained and automatically transfer the client to a ‘real’ adviser.
At a time when client costs and charges are already under close scrutiny, the danger is that high-net-worth individuals and ultra-high-net-worth (HNW/UHNW) individuals may not appreciate the lack of personal touch provided by AI.
Therefore, the general sentiment among wealth management experts is that client-facing AI is not yet advanced enough, nor is it currently appropriate for firms that specialise in wealth management for HNW/UHNW individuals.
This provides a degree of respite for the wealth management firms. The key aspects of the bespoke, personal relationships these firms offer is something that AI currently struggles with, e.g. empathy, responsiveness, the ‘personal touch’ and human intuition.
While such qualities are outside the realm of existing AI technology, the clock is ticking as technology improves.
Nevertheless, what is required clearly goes way beyond the simple Turing test approach to AI, whereby a human would be unable to distinguish between another human and a machine. This, therefore, buys some time for firms in this segment.
As long as HNW/UHNW clients continue to invest enormous sums of money and pay premium fees, it is hard to imagine a time when they would prefer a robot (beyond initial novelty value), rather than an experienced and fully trained individual, looking after their account in a more holistic fashion.
In some ways, the more interesting application of AI is internally within firms looking to improve productivity.
Robotic process automation holds huge opportunities for improving processes and streamlining the middle and back office, particularly when there is long term investment in legacy technologies that do not have the benefit of modern Web APIs for integration.
It is perhaps helpful to think of these types of automation as ‘macros on steroids’, but with the key differentiators of requiring little by way of actual coding, and far greater reliance on machine learning to capture highly complex, yet predictable tasks.
However, it is important to avoid a key pitfall with this technology, i.e. using automation as a ‘sticking plaster’ to compensate for fundamentally broken processes, or poor interfacing and data quality.
‘Chatbot’ technology is another form of AI which is becoming increasing accessible. There are some great Cognitive Services and Data Analytics APIs that can be used in conjunction with existing cloud ‘chatbot’ frameworks to build out proof-of-concept AI solutions.
The key first step is for firms to spend time with their IT department or external IT provider, evaluating the available technologies.
A good starting point is to build out ‘proof of concept’ chat bots that can draw on existing customer data, business processes and wider corporate knowledge, with internal staff providing the initial user base.
The educational and discovery aspect of this initial work can be considered more important than the outcome, and it should be used to provide the inspiration and foundation for any subsequent, more strategic, developments.
Additionally, operational support AI bots can provide ‘expert advice’ to less experienced staff when programmed with a consistent training strategy.
The technology can serve as a mechanism to formalise the capture, harvesting and retention of organisational knowledge, which greatly helps with any succession planning.
Finally, the RegTech space is a large area of AI development, contributing to easing the ever-growing burden of compliance.
The automation of KYC/AML is an example which has seen a good deal of growth.
Firms that are early adopters in the existing technology advances in this area can radically streamline their entire on-boarding process.
Where this is combined with advances in banking Web APIs, new client on-boarding could theoretically become instantaneous.
The robots are coming, and the opportunities for wealth managers are plentiful.