Digital Twins – The Merging of the Real and the Virtual World

Everyone knows the following situation from everyday life. There is not enough time by far to complete all tasks and appointments in time. In these moments, we all wish we had a twin to do these tasks for us. Unfortunately, in the real world, this wish is not granted to us. People and their abilities cannot simply be “cloned” and therefore we have to continue planning and performing our tasks on our own.

In recent years, the work of humans has shifted from the real world to the virtual world due to digitalization and the further development of technical possibilities. Data and information are the focus and are processed with the help of computers. Companies are picking up on this trend. Whereas in the 1970s data was still the result of a process, we can now speak of the “data economy”. In this economy, data takes on the role of an independent product of the company [1].

In particular, the amount of data about real products, processes, and services has increased dramatically in recent years [2]. This opens up new possibilities for planning, simulation and analysis. For this purpose, more and more companies use the concept of a digital twin [3]. The Gartner Hype Cycle also sees great potential in this approach [4].

But what are digital twins and what potentials do they offer at the enterprise level, especially in the financial industry?

What Are Digital Twins?

Originally, the concept of the digital twin originated in the field of logistics. Different components (tires, engine, body) are needed for the production of a car. All these components exist in the real world in the form of physical goods. By capturing and documenting the components, it is possible to virtually design a car based on the data while it is still in production. A virtual representation (e.g., a 3D model) of a real physical good is created, the digital twin, which is stored in a database [5] [6]. While the car does not yet exist in the real world, the already generated digital twin can be used for production planning and control. For this purpose, digital twins are enriched with additional data (historical data, real-time data, physical parameters) [3, 8] on the basis of which the proper course of production is simulated. [6]. By automatically comparing the real-time data from physical production with the model data, deviations can be detected and corrected at an early stage [7]. On this basis, processes in the real world can be modified or optimized [5].

The example of car production shows the linkage and interaction between the two worlds. Virtual twins can be created for existing and future physical objects, processes and services [9]. Thus, potential applications are not limited to manufacturing and the concept can be applied in different industries.

Application Scenarios and Advantages

Basically, there are three deployment scenarios for digital twins in an enterprise [3]. Asset monitoring (1) collects data on physical assets, processes and services and enables their monitoring. Simulations (2) extend this concept and enable testing and planning of different business processes and assets. In addition, digital twins can be integrated into AI-enabled systems (3) that allow automatic detection and correction of discrepancies between the physical object and the virtual twin in real time. In this context, digital twins benefit from convergence with other technologies. Internet-of-Things (IoT) sensors allow real-time data transmission from physical objects to the digital twin [10].

Digital twins thus create benefits at various stages of the value chain. Requirements testing and simulations based on a digital twin enable the efficient design of tangible or intangible objects, such as products (see car example), processes (e.g., individual production steps) and services (e.g., data and service-based business models). In addition to design, the production as well as the use of real goods can be improved by digital twins. In addition, potential maintenance and service orders can be identified at an early stage and handled efficiently [9].

Challenges

In order to use digital twins efficiently and safely in practice, different challenges have to be considered. The most important component is the standardization of the data basis. Data must always be up-to-date and maintained. Therefore, a high degree of automation is essential for data extraction and transmission [11, 12]. Only by using all relevant data can digital twins realize their full potential. In addition to the data, all involved processes must be considered. Only in this way can required physical components (e.g., car – which components and processes must be covered by the digital twin?) be identified and integrated [13]. In the case of interorganizational collaboration between several companies, the companies’ data sources and data must be stored securely and in a trustworthy manner. The use of blockchain technology creates a relationship of trust and transparency between companies [14]. Another advantage of blockchain technology is the traceability of data and its uniform storage.

Possible Areas of Application

The example of manufacturing shows that digital twins are already being used in vertical value chains such as logistics. Other areas of application include the energy and healthcare sectors [10]. Digital twins of wind turbines can calculate damage events in advance and coordinate associated maintenance and service orders. IoT sensors enable the permanent transmission of data in real time to the digital twin. Sensors also play an important role in healthcare. Patient data can be read using smartwatches and fitness trackers and used for patient analysis. Due to the high data density, predictions can be made regarding vital signs and suitable treatment options.

Digital Twins in the Financial Industry

Several approaches to the use of digital twins exist in the financial industry [15]. The following section presents 4 possibilities and examines the impact of digital twins on internal prioritization of projects, risk management governance, tracking ESG goals, and improving sales activities.

If we consider the bank in its entirety as an independent physical object, the individual components (employees, departments, customers) can also be used to create a digital twin. In this case, we speak of a “digital twin organization” [16]. Just as sensor-controlled machines in a factory enable the digital mapping of production processes [17], IoT devices in a bank can, for example, analyze customer movements in a branch, allowing digital advertisements or the deployment of employees to be optimized [18]. Another advantage of such a virtual twin organization is the simulation of different internal decisions. Thus, projects can first be simulated virtually and evaluated at different levels. Above all, the evaluation of the return on investment (ROI) of a project can be carried out efficiently with the help of digital twins [19]. In addition to making the right monetary decisions, the digital project twin enables the simulation of potential problems and challenges in a project. This includes factors such as the configuration of the business case or workload profiles [20]. Subsequently, the bank can choose between the most promising projects. This lowers the overall time-to-market, as banks have access to simulated feedback before product development and project launch, and complications can be prevented before product launch [15]. Examples of projects where digital twins are used for selection include: internal process optimization, product development, and the introduction of new IT systems [21]. Historical data can be enriched with real-time data from the bank, optimizing the performance of projects and processes.

In addition to the internal planning of projects, digital twins can also be used in risk management [16]. Basically, it is very difficult for banks to make risk measurable [22]. Simulating loan repayments using digital twins of borrowers allows the bank to manage its own resources in a more targeted way. Potential loan defaults can be identified early and threats in the network of repayers are efficiently resolved. Customer data and real-time data also play an important role here, revealing the security capabilities of retail customers and other banks. The digital twins of debtors can be enriched with economic, industry-specific, environmental and individual data to feed into the big picture of risk identification. For example, a private individual’s digital twin includes information on his or her reserves, valuables (such as houses or land) and lifestyle [23]. On this basis, conclusions can be drawn about the customer’s creditworthiness.

ESG criteria describe different objectives of a bank in the dimensions: governance, social and environment (ESG). “Environment” includes measures for environmental protection, “social” denotes measures for social commitment or equal opportunities within the bank. Governance includes the sustainable management of the company and the pursuit of the aforementioned objectives [24]. The relevance of these topics has risen sharply in recent years due to climate change and societal changes and continues to gain momentum. It is therefore very important for banks to document and simulate their own progress as well as compliance with ESG criteria. Bank assets and inventory must be periodically tested for ESG criteria. Subsequently, all products and components of the bank receive an environmental assessment and ranking. The bank’s objective is the detailed presentation and analysis of all ESG-related information. In this way, complete ESG transparency can be ensured vis-à-vis the customer and the regulator [25]. An example of the use of digital twins in the ESG context is the analysis and simulation of building emissions. The digital twin of a bank branch or a data center enables the analysis and optimization of energy and resource consumption. The objective is to use renewable energy for operations and to optimize energy efficiency. Here, too, IoT sensors can provide building data (computer, light, energy, heat data) in real time, which can then be used to optimize emissions [26].

The fourth use case for digital twins is in improving bank sales activities [18]. The digital twin of a customer provides information about payments, interests and activities of the real customer. Thus, by analyzing and simulating the customer’s needs, new opportunities for cross-selling and upselling arise. The bank is thus able to forecast future purchases by the customer. These forecasts also flow into product development and make it possible to develop products that are relevant to the customer. The market acceptance of a product can also be tested through the interaction of digital customer twins with the new product [16]. Marketing activities can be planned and coordinated on this basis.

Conclusion

The digital twin approach is not new and has been studied since the 2000s [5]. Nevertheless, the approach is gaining relevance, especially due to new technological possibilities such as IoT, AI, and the blockchain. A digital twin thrives on the timeliness and accuracy of the data used. Through the Internet, this data can be accessed and used in real time. Companies must pay attention to convergence with other technologies when implementing a digital twin, since, for example, evaluation can only be carried out efficiently with the help of AI algorithms. Blockchain solutions are also highly relevant with regard to storage in a cross-company context. Different use cases are already being implemented in practice across industries. Especially in the financial industry, different potentials arise from the use of digital twins.

Nevertheless, the digital twin is not a human clone that substitutes for his or her everyday work. Only by incorporating specialist knowledge and technical skills can digital twins offer the company added value.

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