As early adopters of AI offer their customers bespoke products that deliver intelligent and engaging experiences, those who initially sat on the fringes observing the revolution are now keen to catch up. And quickly.
Following in the wake of such trailblazers as retail, health and the automotive sector, finance is emerging as one of the fastest growing in the quest for smarter, more personalised product offerings.
The value of data
A recent report by Kinsey Global shows the financial sector with a projected 30% growth of spending on AI over the next 3 years. That’s even more than the aforementioned health and retail sectors. But why now? And what is leading this change?
Data has become the new oil of the 21st Century. It’s rich in information and has multiple uses that can fuel industries and empower individuals. It is, however, not something that will be exhausted anytime soon. Quite the opposite actually.
90% of the world's data has been created in the last 2 years with most of this data unstructured and traditionally difficult to utilise.
Optimising unstructured data
Risk management is one of many areas that is currently enabling this type of data by analysing 3rd party news, financial reports, white papers and even social media. By connecting this analysis with in-house risk analysis data it allows robo brokers to obtain real-time insights and early warnings much quicker than humans could complete this task.
With less than 1% of the world’s data ever being analysed, it’s easy to understand the potential of AI with such a rich source of information at its disposal.
Existing pain points
The Finance sector is currently littered with protracted and difficult customer journeys. From buying a home, chasing fraudulent transactions to dealing with call centres and half baked chatbots, the landscape for improvement is both rich and plentiful. The accessibility of new open banking laws has changed the way we can visualise our finances and how this information is shared. Also, the trusted regulation of blockchain and the visibility of historical transactions makes it rich for financial implementation, and advancements in natural language processing have opened numerous doors for additional services and functions offered by digital assistants.
As humans in 2019, we have successfully augmented ourselves with digital technology. Never before has any technology had such a profound effect on the way we live, work and communicate, but still we strive for more. In fact, we expect it.
AI is at the forefront of this expectation. From Spotify finding our next favourite band, Amazon suggesting products that we may like to purchase as well or FitBit telling us to get up and do more, we are already accepting AI as a trusted advisor working on our behalf. Not only does AI offer innovative user experiences, but it also helps us collect and use data required for even further enhancements.
The Finance sector is slowly building on this acceptance with products that can help us manage our budgets better, invest more wisely and alert us every time we make a card purchase. The horizon has already given us glimpses of health insurance obtained by selfies, mortgages for life that adapt to your needs and credit checking that looks at current financial behaviour rather than just historical salary, savings and money borrowed. As customers we demand better products if the established companies don’t deliver, then disrupters (such as Monzo) won’t be long in satisfying that customer need.
Adapting digital teams
With the democratisation and accessibility of AI, machine learning, data-rich API’s and low-cost scalable computing, opportunities are plentiful and resources are rich. But, ironically, it is the shortfall of suitable human talent and resources that is proving one of the biggest blockers in adoption with many big corporations fishing from the same pond.
But even great talent is at the mercy of the quality of data they have to work with. How we unlock its full potential and deliver the insights and experiences it promises requires a new way of thinking. New job roles in fact.
Product teams are currently evolving to best adapt to the opportunities of AI. Data Engineers, AI Scientists, Machine Learning Researchers, AI Engineers, and AI Consultants are fast becoming popular search terms on recruitment portals. Traditional methods of working are also being challenged as well as a monumental shift to user centred design. Lean and agile methodologies are fast becoming the only way to cope with the constant state of flux and speed of growth in AI applications and for the first time, larger financial behemoths are actively replacing slow, cumbersome and heavily documented waterfall and siloed ways of working.
Finance is investing a great deal into AI adoption and looks likely to close the gap on other leading sectors within the next 3 years. The wealth of products, data, and customer numbers lends itself to be a rich landscape for better user experiences and service offering, though getting there will not be easy. The finance sector already comes loaded with great responsibility, impartiality, and governance, so introducing a technology that requires more data, more access and less user friction could be a difficult nut to crack. But as they say ‘Fortune favours the bold’.
Steve Seal is senior CX consultant at Nimbletank