Written by David Dyke, Head of CMC Invest
It’s been quite the year for AI. This time last year, the technology was well discussed among peers working in technology, but it certainly wasn’t a trending topic for consumers globally. Fast forward to now, and 2023 has truly been a breakout year.
Naturally, given the popularity of tools like Chat GPT, industries across the globe are questioning how AI will impact them. What skills will no longer be needed? What can we now automate? How will we teach colleagues and consumers to effectively communicate with AI?
These questions are also relevant to financial services, though we have another layer to consider when making any AI-related decisions – the safety of consumers and their money.
While AI’s sophistication has grown significantly recently, the technology isn’t always reliable. In a beta test, one journalist at The Verge had some interesting conversations with Microsoft’s Bing, where the chatbot claimed it had spied on the company’s employees through webcams on their laptops and manipulated them.
Unreliability isn’t something our industry can risk. Innovation is always encouraged, but we have a duty to do it safely and responsibly.
As AI matures, and industry events such as the AI Safety Summit explore how we can use the technology for good, the financial sector should be able to harness its power with more trust. Let’s explore how.
Uncovering the why:
In PwC’s Global Workforce Hopes & Fears Survey nearly a third of respondents said they are worried about the prospect of being replaced by technology in the next three years. Using AI, however, doesn’t mean you’ll lose your job.
For example, AI could analyse and process huge data sets, pinpointing interesting or unexpected patterns, and enabling financial analysts to concentrate their efforts on areas with the greatest potential.
Rather than providing analysts with the answers, it will identify areas of anomalies and pattern changes, guiding teams on where to concentrate their efforts. In practice, this could involve the technology identifying a period in which a portfolio outperformed. Human analysts could then work to uncover why this occurred and pinpoint successful patterns.
Jargon-free customer service:
2023 has been a particularly notable year for Large Language Models (LLMs). How AI can mimic human conversational traits, and process conversational data has rapidly progressed. As this continues, LLMs will begin to offer human-like live chat experiences, understanding and reacting to tone, context, and intent.
Naturally, we may feel hesitant about losing the human aspect of customer service. However, this could provide more accessibility for financial service customers in a number of use cases.
If AI can mimic the tone of a customer, it can respond in a manner that is understandable to them, removing any jargon and delivering digestible information. Rather than replacing customer service, it may also be used as a tool for colleagues to proofread emails, subsequently providing suggestions on how to respond to a client. Our industry has long faced the issue of accessibility, and AI could help us address this.
Educating customers
Often in financial services, our customers are too scared to ask what they perceive to be “stupid” questions. This isn’t good. To better serve customers the industry needs to know where their knowledge is lacking so we can set expectations and help them achieve the best outcomes.
If customers aren’t asking questions because they feel judged, then AI could help. Rather than having to “confess” a lack of knowledge to human representatives, clients may feel more at ease asking questions of AI-powered “teaching assistants”.
These will be free of judgement, but they could also detect a customer’s level of financial comprehension, meaning that answers can be communicated in a way which is easy to understand.
Understanding consumer behaviours:
Data analysis is already common practice in email campaigns, allowing marketing teams to target consumers at a time they know they are most likely to open and spend time on their emails. While organisations can already define the best time to engage, in the future, AI will help them to understand the why behind consumer behaviour. Having a deeper understanding of this will allow financial services to tailor their product to each person.
This could look like an app telling a customer the best time of day for them to make a financial decision, based on previous successes. Speaking purely in hypotheticals, history might indicate that someone’s best stock decisions have been made at lunchtime, while they may be most likely to be in a saver mind-set at the end of the day.
The future:
Change is a given, but the speed at which it will come is hard to predict. Financial services has a responsibility to ensure any new technology is delivered safely, and ethically. So rather than assessing how quickly we can innovate, the focus should be on leveraging a tool we understand and trust.
