Artificial intelligence (AI) is a term used liberally and to mean different things. When we talk about AI, we are usually describing computers taking on tasks that would typically have been undertaken by humans.
At Bravura, we see AI as a broad approach to tackling different challenges rather than a specific type of technology. A lot of hype surrounds AI because innovative approaches are emerging all the time, but most of the technology that is categorised as AI, is machine learning (ML), a branch of AI.
These technologies are able to accurately and effectively complete tasks that, in the past, would have taken humans much longer. JP Morgan’s [AI technology] for example can review approximately 12,000 documents in a matter of seconds…a human would spend 360,000 hours on the same documents.
The potential for the broad church of artificial intelligence applications is vast and there are certainly opportunities across financial services, with particular benefits for financial advice:
Encourage human creativity
ML can be used effectively to take on laborious and often repetitive work that requires a high degree of precision and accuracy. This will free up human capital from monotonous and repetitive tasks , allowing them to add value by focusing exclusively on the needs of the client. When it comes to financial planning, for many people, having an adviser who can exercise human emotion is crucial as our own emotional decisions – often impulses – can have a huge impact on our finances.
For instance, if a customer is looking for financial advice and has experienced or experiencing an emotional event, such as a death of a relative to plan for, ML will not currently be able to take the bereaved state of mind into account. Whereas, an adviser will be able to apply emotional rationale to the planning. However, this is not going to be the case forever. Machines understanding emotional intelligence and deep empathy is no longer a thing of science fiction. Concurrent with the progress of AI in the last two decades, ‘emotional intelligence’ has also developed significantly with advances in neuroscience and tools.
However, until developments in these areas are widely available, in order to sufficiently implement ML, businesses would need to identify where machines are better suited to the work and where humans add value, as getting it wrong, could have a detrimental effect on overall customer service.
Opportunity for advisers to offer better advice to more customers
The fact find is a vital part of the advice process as by getting this information advisers can understand the individual client’s circumstance and the complexity of their financial affairs. The advice given after this is highly tailored.
It is noted in the industry that younger customers with less wealth, or those with less complex financial needs, may be happier to interact with a ‘chatbot’ or a robo-adviser and that for all customers, natural language processing (how a computer processes and analyses large amounts of natural language data) is making it increasingly difficult for customers to tell whether they are talking to an AI interface or a human. According to research from Gartner, by 2022, 70% of white-collar workers will interact with [chatbots] daily. This expected growth is on par with the increase of millennials in the workplace.
Additionally, using ML as part of the fact find to collect and analyse more data will better equip advisers with information on their clients. This can only be beneficial for the advice process and may later result in improved results for the client.
Potential prevention of human error
ML can analyse and collate many data points at a far more effective rate than humans can. Humans are fallible, and error – although unlikely in professional advice circles – cannot be discounted. Technology can negate the chance of this happening as it is able to perform such data harvesting tasks at a much higher rate of accuracy. One example of this could be predicting clients that would be most likely to want to withdraw assets in a market downturn and putting them on an adviser call list. ML could be used to measure ‘volatility anxiety’ and flag these clients to the advisers as potentially needing extra support.
Above we have eluded to three ways AI and ML can improve advice however, as these technologies improve so will the benefits. As ML becomes more powerful it may be able to replicate human emotions and will therefore be able to aid further in the process. We do not believe that AI will be able to fully replace human advisers but instead will ensure that customers receive the best possible advice tailored to their individual needs.