Caroline Hermon, Head of Adoption of Artificial Intelligence and Machine Learning at SAS UK & Ireland
It's no secret that challenger banks and fintech information mill changing the banking industry as we know it.
The main issue for today's banking leaders isn't that customers are switching to new high-street providers, such as Metro Bank, or to internet and telephone banking specialists like First Direct.
The real threat is much more insidious – and much harder to combat. A wave of next-generation digital disruptors, for example Monzo Bank and Revolut, are launching brand-new types of services that eat into the most profitable parts of traditional banks' value chains.
Agility, flexibility and an insatiable appetite for innovation are powering an upswing of these disruptors. Many traditional banks claim that they can value these same qualities, yet only a handful of today's market leaders practice the things they preach in their digital strategy documents.
Banks that can't read the writing on the wall risk becoming obsolete to see their market share whittled away. With data-driven innovation speeding the industry towards a future of seamless, integrated, customer-focused services, it really is time to get real or get free from the race.
Overcoming barriers to change
Many traditional banks are extremely focused on keeping the lights with that they fail to execute their innovation goals. Keeping a bank running profitably while satisfying the regulators isn't any easy task. And transformation initiatives frequently get pushed down the priority list.
Moreover, within the race to deliver transformation, a lot of lenders fall at the first hurdle. Their main competitive advantage – the rich data they possess about every facet of their customers' financial needs – is typically siloed in multiple legacy systems and managed by different departments. The task of consolidating data and creating a central hub for business analytics is pricey, time-consuming and diverts valuable resources from run-the-bank activities.
Resistance to change is another common barrier to innovation. Automation can be an unwelcome concept for decision makers, particularly those who manage large teams and also have spent years building up expertise round the bank's legacy systems and manual processes.
But it does not have to be this way. Banks that adopt a disruptor mindset notice that machine learning and artificial intelligence are powerful tools. Using the right approach, these banks may use the same technologies to both drive transformation initiatives and streamline day-to-day operations, developing a virtuous circle.
For example, analytics-powered automation is not just a tool to eliminate paperwork and lower headcount. By freeing up time, it can also help employees focus on high-value transformational tasks, revealing new opportunities for product development and highlighting ways to build more customer-centric services.
Finding a winning formula
Traditional banks may find transformation painful. But when they do embrace change, the advantages can be huge. SAS works with many of the leading banks in the world today, helping them develop and execute effective analytics strategies that build on their success and strengthen their ability to compete.
Analytics is key to transformation since it is one of the unique competitive advantages that large, well-established banks have over their new rivals. With countless customers who trust and value their services, these banks can accumulate a vast amount of incredibly rich data about customer behaviour.
Since modern analytics techniques such as deep learning are incredibly data-hungry, the banks with the most data will be able to build predictive models that are far more sophisticated and accurate than their competitors. This could prove to be a decisive advantage as AI initiatives start to take centre stage in transforming customer service.
Transformation in practice
Recent success stories illustrate the very real rewards that banks can reap from digital transformation. For instance, SAS has helped RBS put timely, accurate and insightful data analysis in the centre of every decision and use customer feedback to improve its services. The bank is on track to become the UK's No. 1 bank for customer support.
Similarly, Nationwide recently worked with SAS to enhance member communication. Using AI and natural language processing of customer emails, your building society identified the communication methods that produced a positive reaction – and people who created frustration.
The analysis says 26% of all interactions could be gone to live in an online process – reducing waiting times for purchasers while saving time and resources. What started as a proof of concept has now become a companywide initiative to use data analysis to streamline its back-office operations, develop new products and evolve its services.
Examples such as these show that with the right analytics strategy, banks do not have to make a choice between keeping the lights on and driving transformation. When banks mobilize their talent, data and expertise, they are able to combine innovation with efficiency and the disruptors at bay.