The future of banking – new data practices and structural change from the ground up

By Viren Patel, Financial Services Industry Strategist at Workday


Retail banks are fundamentally different now compared to ten years ago. Customers have much more agency and manage their finances through apps designed to make banking as easy as using any other app. Banking has truly become tailored to the customer.

It’s easier than ever for users to open accounts.  And they can share key financial information between applications and institutions at the click of a button with Open Banking, while their money is kept safe with convenient, secure biometric authentication.

All these changes and evolutions are the result of new customer-facing technologies which have been implemented in banking in recent years.

What caused this changed?  The transition has really been driven by the fact that digital natives have come to expect a slick, reliable digital experience. In banking, challenger banks have provided them with exactly that. The mainstream has thus had to follow, trying to match this experience while lowering operating costs.


And the hidden wiring?

However, we get a totally different picture when we look beyond the customer-facing technology, specifically at mainstream banks’ back-end systems – what we might call the hidden wiring.

Most banks still run on legacy systems and old data models. According to Deloitte just 11% of banks have modernised their core systems to the point that they can easily integrate emerging technologies.

How do banks benefit from modernising their back-end and how do they do it?


Banking – solving the data issue

Data modernisation is a non-negotiable when it comes to transforming back-office systems. Most bank back-office activities – credit and affordability assessments, process management, document management and compliance; audits – rely on data. But legacy back-office systems and old, siloed data models create problems.

To begin with, legacy systems usually force banks to manage their workload as a series of disconnected accounts and transactions rather than a single customer. They cannot always get an accurate and timely view across their loan portfolios, and they could gain much more insight into the value each customer and product truly represents if they could.

Furthermore, it can be difficult for banks to collate the data needed to meet audit requirements. Regulatory reporting often takes longer than it should given the need to use multiple systems. As a result, vast amounts of time is wasted every month closing the books and reconciling data.

Finally, although banks hold vast quantities of customer information, it is often siloed. Most can’t consider data holistically. If it was consolidated up, this data could deliver important insights. But that is often not possible and so those insights rarely materialise.


Finding insight in back-office data: from silos to strategic insights

How might it look if this data was coherent, in one place and available for interrogation?

Customer, product and market data could be mined for insights to guide decision-making.

At the same time, operational data could be analysed in tandem  with finance data, in real-time. This could deliver a real-time view of the value of each customer on the bottom line. It could also provide insight into new products to offer and potential markets to enter.

This insight has incredible value to the businesses that manage to acquire it. We’ve gone beyond the ‘new normal’, to a world some have defined as ‘never normal’. The old certainties no longer prevail. In this changing market, insight and agility are business imperatives.

Real-time understanding and insights are thus crucial to banks.  And banks also need to be able to model and forecast multiple scenarios continuously.

Financial institutions that rely on episodic planning and legacy systems will have limited insight. They waste valuable time and expose themselves to more risk. They won’t adapt as quickly to the changing world around them.

Product profitability is a great example. Historically, it has taken a lot of time and effort to see clearly whether a product is profitable. It has required huge amounts of data manipulation in the background.

With modern data techniques, banks can see precisely how profitable each product is at any given time and forecast profitability with more confidence.

Also, instead of seeing each customer as a series of disconnected accounts and transactions, and making decisions based on their use of a single product, banks can also be able to see the customer as an individual. They’ll see their behaviour and their risk or value in its entirety, and understand more and more clearly each customer’s needs.

This decreased risk while allowing banks to tailor their offering in unique, attractive ways.


The value of machine learning and AI

The majority of these gains will involve using artificial intelligence (AI) and machine learning (ML) to find insights from huge data sets.

Research by Workday shows that nearly three-quarters (73%) of business leaders already feel under pressure to implement AI in their organisation. And for  finance, research from The Bank of England and Financial Conduct Authority suggests that 72% of UK financial services firms are developing or deploying ML.

That same report predicts that the number of ML applications used by organisations in the sector will grow 3.5 times in the next three years.

But both AI and ML require coherent, complete data sets. If banks want full value from these technologies they need to address their back-office data and modernise the hidden wiring.

Back-office modernisation = leaner operations

Digital first rivals are putting pressure on banks, and data-savvy tech firms can cherry-pick some of the most profitable bits of their business. Where historically banks required ownership of the current account to gain the greatest customer insight, firms can now gain at least the same customer understanding through open banking and by bringing together other alternative data sources, allowing them to focus on the more lucrative financial products.

Key to navigating this change is operational efficiency. Having data that is AI- and ML-ready allows the use of technologies like robotic process automation to streamline burdensome processes. Modernising back-office systems means smoother, more efficient operations, with staff freed up to focus on work that adds value, rather than routine administration.


Bridging the gap

Improved customer-facing technology and the gains they lead to show that banks can innovate and drive progress that has significant benefits. IT is now the time to extend progress through the mid and back office, gaining new and improved insights from joined-up systems that provide the most complete picture of customers, portfolios and business performance at any given time.

Institutions that pull this off quickly and well will be best placed to adopt new technologies such as AI and ML, make better customer and business decisions and embrace new ways of working to drive their organisations forward.




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