Institutions from across the banking sector are turning to digital twinning to help them with a range of challenges.

A digital twin is a virtual representation of a real-life entity or system, created in software. Digital twins can take many forms, but the essence is invariably about capturing and using data in a way that accurately mirrors the physical world. The idea is to use the twin to simulate conditions, test scenarios and explore what-ifs. Deploying a digital twin can allow you to collaborate virtually and predict outcomes. 

The use of digital twins is already common in a number of vertical sectors. Manufacturers use them as tools to help optimise value chains, while energy companies collect massive amounts of data from the field to build complex digital models which can be deployed, for example, in the planning of an underwater drilling operation. In healthcare, it is possible to create accurate digital twins of the human heart for clinical diagnoses as well as training. Singapore has even built a detailed virtual model of itself to help with urban planning and disaster recovery preparation. In Formula 1 racing, teams like Mercedes-AMG Petronas Formula One Team use digital twins to reinforce near real-time/fluid decision processes, this requires compute power and vast amounts of data to run multiple scenarios. 

So can this concept work in banking? What purposes might it serve? And why is now the right time to be talking about it?

The fact is that banks and other financial institutions are facing multiple pressures from several directions, giving them every incentive to innovate with fresh approaches. All are managing an explosion of data from across their business. There is a great deal of regulation around this data, not to mention ongoing uncertainty in many cases about who owns it and where it should reside. The era of Open Banking has handed these organisations another layer of complexity, demanding new thinking on managing engagement with customers and influencing the customer journey. Competition is coming at traditional banks from every side, much of it digital and more agile than they are. Meanwhile disruption from Brexit and COVID-19 are also part of the landscape, probably for the next few years.

Let’s consider a few possible use cases where a digitally created scenario might help to deliver a competitive edge in a banking context. 

Some banks have already experimented with creating digital households to explore different lending outcomes. This allows them to consider how their risk is affected by a variety of scenarios, for example calculating how a change in interest rates might have consequences for spending within a particular household.  

A digital twin can be made not just of a process or a location but of an individual. Thus a digital twin of an employee or group of employees can help judge the effect of a change in where and how they work, as well as how they are engaging with different processes within the organisation. 

In asset management and capital markets, the use of digital twins might be used to explore responses to a new product in advance of its launch. Or it could help evaluate the effect of different market conditions on employees. In retail banking digital twinning is more likely to apply to the customer journey and customer-related processes. It can allow banks to test out new market scenarios in real-time, allowing, for example, a faster response to a customer loan application and better outcomes from a range of touchpoints. This mirrors how the airline industry has been using digital twinning for a number of years.

In most areas of banking, digital twinning could be part of speeding up time to market. It could allow a bank to prioritise the initiation of projects that have been proven to work digitally, testing first if they deliver the kind of returns that are needed. Twinning might be useful in the area of acquisition, or in cross-selling an existing product into a new market. Spearheading innovation in areas that are proven to work digitally has the potential to change the whole dynamic of a banking business. It could not only improve profitability but contribute in areas like security, compliance and governance as well. 

Those with their ears close to the ground might be aware that digital twin capabilities are not entirely new. For nearly 20 years, they have been a part of helping to improve products and processes. But advances in artificial intelligence and machine learning, as well as the handling and analysing of larger and more complex data sets than ever before, mean that today’s digital twinning is a different ball game to those early forays. Use cases are consequently proliferating in all sorts of new directions.

Early digital twins were more about future potential than immediate benefit. Many of the ideas that companies wanted to implement were limited by available connectivity and compute power. The data storage and bandwidth required to process the massive volumes of data that twinning involves were either not there or were cost-prohibitive.

But now the trend is truly picking up momentum with new simulation and modelling capabilities available, better interoperability between systems, improved IoT sensors, and much faster, more reliable and cheaper computing infrastructure. Digital twinning capabilities are accessible to organisations of all sizes and across industries. Richer data is leading to richer, more dynamic and more accurate virtual simulations. AI and ML are supercharging data visualisation, computation and the running of scenarios and managing this at a scale that humans can’t come near unaided.

The next step, of course, is to take all this technology and apply it pragmatically to achieve concrete results. That’s the next frontier for the digital twin, and one that is probably best crossed with the right partner. An appropriate partner can help deliver the full promise of digital twinning by integrating systems and data across entire organisational ecosystems.