Digital Twin Technology
Revolutionising Operational Resilience
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On 31 October 2023, GreySpark Partners first referenced a revolutionary solution that could help financial firms create a robust operational resiliency model amid growing regulatory pressures.
That solution is a digital twin model.
A digital twin is a software programme that creates an exact virtual representation of a real-world entity or process by leveraging AI techniques. Digital twins are dynamic, in that they are linked to data sources from the real, operational environment and access that data in close to real time. This means that the digital twin immediately reflects any changes to the operational environment. Data outputs from real-world processes are run through the digital twin software, which generates an evaluation. The feedback is analysed and the analysis is tested, so that the process itself is improved and the cycle iterates toward optimality. The result is a hyper-realistic virtual model of a system that can test typical stress scenarios, generated in seconds without the need for significant human intervention.
Digital twinning is not a totally new concept. According to a survey by Altair, 71% of respondents in the banking, financial services and insurance (BFSI) sector stated that their organisation already uses digital twin technology. Also, 64% stated they are ‘highly knowledgeable’ about digital twin solutions.
Interestingly, while the Altair survey revealed that of the respondents who stated that they do not currently leverage digital twin technology, just 4% expect their organisation to adopt it within the next six months. This is an intriguing finding, given that most organisations that use digital twins say it is very important to their operations. As operational resilience regulatory pressure grows, which you can be reminded of here, financial firms that do not utilise digital twin capability will find themselves unable to comply robustly with the operational resilience regulatory mandates.
A digital twin can be used to quantify the macro impacts of system changes, such as financial stability. The technology can also be used in stress testing to ensure the safe functioning of the system under bespoke scenarios, and to evaluate the extent to which changing the parameters of system features and design can reduce risk and / or optimise performance. While traditional scenario testing simulations and digital twins both use digital models to replicate products and processes, there are some key differences between the two. Digital twins are arguably better from a quality standpoint. Simulations are run in virtual environments that may be representations of a physical environment, but do not integrate real-time data, and theoretical data parameters are set before the simulation is run. In this sense, simulations are largely static and as a result, there are limitations on the number of different scenarios that can be simulated, realistically.
In contrast, digital twins use AI and automation to integrate real-time data and create an exact virtual representation of a process, updating in line with adjustable data inputs thanks to their dynamic nature.
Overcoming Operational Resilience Challenges
Digital twins can help financial firms to address operational resilience challenges on four fronts;
Resource Constraints: Testing is commonly undertaken out-of-hours because production environments cannot perform their essential daily functions and be tested at the same time. Typically, financial institutions address scenario testing requirements by employing more staff (i.e. testing teams) in the hope of speeding up the testing process. This results in increased staffing costs and can lead to ‘analysis- paralysis’, and divert attention from core business practices. Digital twins co-exist with the production environment and require minimal human intervention thanks to the incorporation of automation / ML, which can also mean that the speed and frequency at which certain scenarios can be tested is increased;
Data Silos: A typical data challenge that firms face is the lack of sufficient data to test the various scenarios, as well as the limitations of a human test designer to create a sufficiently wide variety of scenarios to test the potential impact of unforeseen events. The use of cleansed, plausible real-world data in the digital twin model eliminates the effort associated with the cleansing and validation of data specifically for testing in the simulation approach. In addition, digital twins can break down data silos across different functions (e.g. across Business, IT, Security and Risk teams and across teams in different geographies) and unlock value across the product life cycle, because it aggregates historical data and real-time data in one place. This encourages a cohesive, universal data management framework, engendering a more holistic, simplified data view of the firm;
System Vulnerability: Banks and other financial institutions are constantly under threat from costly and damaging cyber-attacks. The dynamic view of systems and processes facilitated by the digital twin can enable organisations to detect potential security threats in real- time and take timely action to protect their customers and assets. In addition, it allows businesses to gain greater insight into their operations without having to invest heavily in infrastructure upgrades or additional personnel resources, while also reducing future system downtime in
the case of an unforeseen event.
Third-Party Risks: Given that the operational resilience regulatory landscape is still evolving, it is critical that financial firms utilise an agile, configurable platform that can support dynamic real-time data inputs and allow them to meet new requirements as they arise. Achieving regulatory compliance for testing can be challenging due to the reliance of financial firms on third-party systems. In particular, oversight of third-party providers is important to ensure that any contracts between financial firms and third-party providers are compliant under regulatory frameworks such as the Digital Operational Resilience Act (DORA).
For example, financial firms must inform the supervisory authorities of any ICT services that support critical or important functions and that due diligence of third-party services has been conducted. Third- party vendors are not always willing to provide critical information to their financial firm clients, or participate in the scenario testing that financial firms are mandated to do because the vendors have little incentive to do so. In deploying a digital twin, banks have something to entice third-party ICT service providers. Many vendors only sell a single product into a bank, whereas the bank will have numerous third-party products embedded into their IT landscape across the enterprise. To create the digital-twin, all third-party vendors are included, and banks can allow the third-parties to see how their solution interacts with the other platforms that the bank has. This information can be very advantageous to third-party vendors to use in their own testing phases, meaning they will be more willing to work with the bank in its third-party risk assessment.
Stay tuned for more digital twin insight this week!