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FAST-TRACKING THE EVOLUTION OF BANKING WITH AI

08/07/2019

By Tiffany Carpenter, Head of Customer Intelligence Solutions at SAS UK & Ireland

 

How intelligent decisioning solutions can help you stay relevant in the era of digital banking

Fierce competition, advances in technology, and consumer expectations for hyperpersonalised services are forcing the financial services sector to evolve. To adapt to rapid market developments, many banks and insurers are launching ambitious digital transformation projects. But do they actually deliver results?

 

The short answer: not often. In a recent study, McKinsey & Company found that fewer than one-third of organisational transformations succeed at improving a company’s performance, and a staggering 70% of large-scale change programmes don’t reach their stated goals. That’s a lot of effort and upheaval for very little reward. So what can we learn from this unsettling trend?

 

Common pitfalls

Every business and digital transformation strategy is unique, but there are four avoidable mistakes that financial services companies repeatedly make when approaching digital transformation:

 

  1. Misunderstanding the challenge

Whilst almost every financial institution has some kind of digital transformation strategy in play, many are focused on the technical aspects of digitisation and adapting to new channels and tools.

 

For example, many business leaders believe that digital transformation is mainly about replacing manual processes with automated workflows. That’s why there has been a rush to invest in robotic process automation (RPA) across the big banks and insurance players.

 

However, while automation can play an important role at the implementation stage, digital transformation is much more about reimagining traditional business models to succeed in new, fast-changing digital economies. In a banking context, that means redefining products and services to reflect the realities of a market where the customer is king.

 

  1. Pursuing disjointed initiatives

When rolling out digital transformation projects, banks often focus on innovation in individual functions or departments without considering how changes on one side of their business will affect other areas.

 

That’s a problem because banks have traditionally been structured along vertical product lines such as current accounts, savings, mortgages and credit cards, and horizontal business functions such as marketing, technology and finance. As a result, change programmes inevitably get stuck in the interdepartmental crossfire.

 

Instead, digital transformation initiatives must seek to disrupt the complex legacy operating model of the traditional bank and replace it with a more holistic, customer-focused approach.

 

  1. Making data unreachable

The letter “d” in digital transformation should stand for data. Without the ability to collect, store and access data, and the tools to refine it into actionable insights, banks won’t be able to leap ahead of their competitors.

For example, in an effort to serve business units with fast access to key information, many banks have established centralised data lakes. While this approach succeeds in eliminating individual data silos, it often ends up replacing them with a single large silo that is equally inaccessible.

 

Placing all data under the stringent governance of the IT department can make it very difficult for other business units to access and analyse time-sensitive data quickly. This can limit the value of insights and diminish the return on investment for large-scale change initiatives.

 

  1. Enabling cultures of resistance

The most challenging aspect of transforming any business is inspiring its employees to become advocates for change. Inertia, doubt and cynicism from people within the bank can stop transformation initiatives dead in their tracks.

 

That’s why setting a clear vision for change and encouraging employees to experiment with new ways of working is essential for banks to achieve a smooth adoption of new technologies and processes.

 

Shopping for success

In designing successful transformation initiatives, banks and other financial institutions can learn a lot from companies in other sectors that have harnessed analytics to avoid falling foul of these common pitfalls.

 

Take Shop Direct, which is not only the parent company of retail brands such as Very and Littlewoods, but also one of the UK’s largest nonbank lenders. While the company had thrived for many years on its traditional catalogue-based sales model, it realised that the future was not in paper. To pivot the business and remain relevant, it had to establish an online presence – and fast.

 

Shop Direct knew that moving its retail and financial services businesses over to an online-focused operating model would not be easy, but it had a secret weapon: vast amounts of data on customer buying habits, sales information and inventory records.

 

Within 12 months, Shop Direct built a solution based on intelligent decisioning software from SAS that was capable of mining useful insights from more than two years of data of customer interactions. By combining historical data with real-time context such as browsing behaviour, the company can now make instant decisions to tailor the user experience for each customer: personalised sort orders, personalised recommendations and real-time credit risk decisions.

 

Intelligent decisioning in banking

Similarities between the challenge faced by Shop Direct and the current ambitions of traditional banks are striking. Banks face an urgent need to reinvent their traditional business models for the digital world. Moreover, banks also possess huge volumes of customer data that they can analyse to find valuable insights about how to enhance existing services and develop new products.

 

AI and machine learning technologies have the potential to help traditional banks transform – but analytics on its own is not enough. Banks need to harness the insights generated by analytics to automate decisions at scale.

 

This means basing analysis not just on departmental data sets, but on all the information the bank possesses. It needs to include both historical transactional records and live data streams that provide immediate context on customers’ behaviour and actions. Furthermore, the analytics needs to take place in real time and drive automated actions to respond to immediate customer needs in order to truly affect the customer journey.

 

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