Digital twins is a widely misunderstood concept. Surely, they are simply digital representations of the physical that utilise the ‘Internet of Things (IoT)’ to allow organisations to monitor and report? Well from one perspective the answer is yes, but there is much more to the concepts and the use of this capability that may initially be evident.

Wikipedia defines a ‘digital twin’ as a virtual representation that serves as the real-time digital counterpart of a physical object or process. It is a concept that has been around for over 50 years with the first documented example being the Apollo programme, which developed digital models to allow scenario testing prior to implementation in the real world. This concept literally saved lives when, in April 1970, the Apollo 13 mission faced almost certain disaster. By using a digital model of the lunar module, NASA was able to ‘test’ multiple fixes before physical execution in a ‘get it right, first time’ opportunity. This approach ultimately led to the safe return of the astronauts back to earth.

Today the concept of digital twins is widely adopted in manufacturing and engineering. Examples include large-scale power production, jet engine testing and high-performance vehicles, such as Formula 1. This has resulted in the concept being extended across many other industries with analysts predicting 35% growth in value between 2021 and 2027 to $50 billion. This is fuelled by the adoption of IoT, with 75% of organisations employing IoT also planning to adopt digital twins.

Is that it – or is there more to it?
At one level, digital twins combines a digital representation of assets and relationships, which together can be described as a digital nervous system. However, the real power is released when this digital nervous system is combined with a digital memory and brain – the ability to make decisions based on external stimuli – through learned experiences. This is a concept developed and first explained to me by my colleague, Simon Turner, who described how the power of digital twins is released when it is combined with data platforms that provide a corporate digital memory and brain enabled by data science. Together, these elements allow organizations to fully exploit the power of digital twins.

But what does this mean for financial services? Here are four examples.

1. Digital twin organization (DTO): DTO lets banks model and assess the impact of transformation initiatives. This creates the nervous system of an organisation and uses its corporate memory to develop learnings based on new experiences. For example, customer onboarding could be modelled around current interactions and past performance to improve the process. Likewise, a similar approach could be adopted for new product launches.
2. Branch and channel management: Digital twins could be used to optimise and model the impact of changes to channel mix to customers and colleagues. Using branch and channel models and data around process, skills and experience a bank will be able to design the optimal channel mix for new engagement before implementation in a physical world.
3. Risk management: The ability to model risk could be applied to lending, especially the performance of loans given to businesses and consumers over the last 18 months. Creating a framework for predicting payback will help a bank proactively manage reserves and strengthen its balance sheet. Likewise, by modelling the network and security capability of a bank, it is possible to assess new threats quickly and effectively.
4. ESG: The ability to model, monitor and report climate risk exposure is becoming increasingly critical. Especially as there are differences between alternative modelling approaches, a variety of climate rating agencies and the emissions data reported by non-financial companies is unaudited. Stress testing and scenario planning is key so by applying digital twins it will be possible to better understand the impact of climate change.

These are just a few examples. I expect to see many more. In the meantime, the opportunity remains to drive innovation, reduce cost, and minimise risk through the development of new use cases. Now is the time for investigation and experiment. Microsoft have democratised the ability to produce digital twins with the provision of cloud native data and data science platforms – making it easier than ever to deliver the corporate digital nervous systems, memory, and brain of the future.

If you are looking at this topic, please reach out as I would love to have a conversation around how we can work together to develop digital twins in the financial services sector.