Simon Farthing, Head of Consultancy at data science consultancy Profusion, discusses how financial institutions will use data in 2016
Financial institutions are often held up as an example of a sector that is slow to adopt technological change. Indeed, the explosive growth of FinTech start ups in London is often cited as a direct result of a lack of innovation in the City. However, the reality is that many banks are moving quickly to capitalise on the opportunities offered by a new technology. This trend was particularly apparent this year with the rapid adoption of mobile payment services such as Apple Pay.
The technological challenge of integrating Apple payment systems and infrastructure into most major banks cannot be understated. Add to this the phenomenal growth of contactless card payment and 2015 was a year of profound change in how people pay for things. However, payment trends is just the consumer face of FinTech, a lot of the major tech changes happen away from the public eye.
This year data science tentatively entered the mainstream via the growing interest in machine learning. More people were waking up to the fact that their Amazon recommendations or the self-driving cars they saw on the news were powered by a form of artificial intelligence. For financial institutions, machine learning has played a major role in automated trading for a number of years. 2015 saw the continued growth of ‘robo-advisors’ – platforms of online tools and portfolios of ETFs that automatically rebalance.
The algorithms that underpin these systems are largely powered by data science. Indeed, the financial industry is one of the pioneers of data science techniques. Although, the adoption of data science has been far from uniform across all banking services. In 2016 I expect this picture to change. Better use of data and personalisation of services will move from the financial markets to retail banking. It will have a profound impact on marketing, customer service and product development.
Atom Bank has already announced its intention to use data models to predict its customers’ needs. It’s worth noting that Atom Bank’s model of prioritising mobile services over bricks and mortar branches is, in the long-term, likely to be adopted by most major banks in the UK. However, such a move will require large scale investment in IT infrastructure, something that is notoriously difficult to get right in financial corporations with bespoke legacy infrastructure.
The drive to more mobile and automated services may provoke some banks to increase spend on technology infrastructure, however, do not expect 2016 to usher in the age of ‘Uber-banking’. Change will be incremental and fuelled by the purchase of promising FinTech start ups. The difficulty many financial organisations have in creating a culture of innovation in-house is not going to disappear overnight. Nor are we likely to see a young up-start ‘disrupt’ the banking sector, the problems surrounding funding and scaling make the financial sector a particularly hard nut to crack.
The drive towards personalisation and automation could take a number of interesting turns in 2016. Banks could install beacon technology throughout their branches to gain customer intelligence. Email marketing or push notifications could take a highly targeted form as mobile data is married with other data sets such as weather, major events, economic news and customer service information. As more data science techniques are applied, financial organisations will learn more about their customers and be better placed to predict future needs. This growth in knowledge could mark the creation of entirely new financial products. The end-goal would be utterly personalised financial products that update dynamically based on new information or the predicted changes in the customers personal situation.
Such is the opportunity offered by data-driven banking that financial organisations will increase their investment in data science and data infrastructure – especially in their marketing departments. Of course, there are a few hurdles to overcome, data collection, protection and access policies and procedures will need to change. The first step towards this goal is wider data-led education processes that give in-house marketers, customer service representatives and even in-branch employees greater knowledge of how data can be used to advance targeting and personalisation.
2016 is not going to see a revolution in how the financial services sector operates, however, some organisations will pull out ahead in how they use data to improve the services they offer customers. For consumers, an increase and refinement in the services they are offered online, especially on mobile, may cause a significant reduction in the reliance on branches.