Caroline Hermon, Head of Adoption of Artificial Intelligence and Machine Learning at SAS highlights the challenges established banks are facing in an increasingly competitive, digital world and the need to utilise innovation
It’s no secret that challenger banks and fintech companies are changing the banking industry as we know it. The main issue for today’s banking leaders is not 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, such as Monzo Bank and Revolut, are launching completely 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 the rise of these disruptors. Many traditional banks claim to value these same qualities, yet only a handful of today’s market leaders practice what they preach in their digital strategy documents.
Banks that can’t read the writing on the wall risk becoming obsolete and seeing 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 out of the race.
Overcoming barriers to change
Many traditional banks are so focused on keeping the lights on that they fail to execute their innovation goals. Keeping a bank running profitably while satisfying the regulators is no easy task. And transformation initiatives often get pushed down the priority list.
Moreover, in the race to deliver transformation, many banks fall at the first hurdle. Their main competitive advantage – the rich data they possess about every aspect of their customers’ financial needs – is typically siloed in multiple legacy systems and managed by different departments. The task of consolidating data and establishing a central hub for business analytics is expensive, 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, especially those who manage large teams and have spent years building up expertise around the bank’s legacy systems and manual processes.
But it doesn’t have to be this way. Banks that adopt a disruptor mindset recognise that machine learning and artificial intelligence are powerful tools. With the right approach, these banks can use the same technologies to both drive transformation initiatives and streamline day-to-day operations, creating a virtuous circle.
For example, analytics-powered automation is not just a tool to eliminate paperwork and reduce 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 benefits can be huge. Analytics is key to transformation because it’s one of the unique competitive advantages that large, well-established banks have over their new rivals. With millions of 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 extremely 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 begin 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 example, SAS has helped RBS put timely, accurate and insightful data analysis at the heart of every decision and use customer feedback to improve its services. Now the bank is on track to become the UK’s No. 1 bank for customer service.
Similarly, Nationwide recently worked with SAS to enhance member communication. Using AI and natural language processing of customer emails, the building society identified the communication methods that produced a positive reaction – and those that created frustration. The analysis revealed that 26% of all interactions could be moved to an online process – reducing waiting times for customers 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 like these show that with the right analytics strategy, banks don’t have to make a choice between keeping the lights on and driving transformation. When banks mobilize their talent, data and expertise, they can combine innovation with efficiency and keep the disruptors at bay.
