Saying that the financial industry has experienced systematic shifts would be an understatement. It is not surprising that people are looking for financial services rather than banks or insurance companies. Technologies, such as blockchain, big data, and artificial intelligence, are changing the way companies in the financial sector operate, not to mention that the pressure from innovative fintech startups is forcing business model redesigns. The recent pandemic accelerated the ongoing trends even further, pushing banks to shut down much of their face-to-face interactions with clients, step up virtual operations, and speed up the operating model change.  Most incumbents in the industry are unable to keep up with the pace of change, and they are being held back by what once gave established financial companies an advantage: the perception of “experience,” the emphasis on stability rather than agility, and the expensive physical presence of branch networks.
In this article, we will explore what innovations the operations digital twin can bring to the financial services sector and how digital twin can alleviate current industry challenges.
Digital twin for asset risk assessment
The financial industry is fundamentally all about risk. Therefore, the core industry capabilities are geared toward efficient assets risks estimation, asset valuation, understanding how customers plan to use an asset, and how to finance and protect it.
Imagine how powerful it would be if a risk model automatically considered a state of an asset given construction details, the maintenance history, and other useful parameters. Such a perspective would enable a more accurate valuation of the property. Therefore, the insurance premium could be different given the new valuation. For example, insurers can tailor risk models if they know the plumbing is 40 years old. As a result, preventive maintenance services may be required in this case due to the likelihood of water-related damages.  The notion of risk and value can become dynamic, will evolve with time, and will depend on asset exploitation and externalities. Such a shift can open many doors of innovative business models around traditional commoditized financial services.
Digital twin for operations innovation
Customers are moving to digital channels faster than they have done in the past; they expect a high level of personalization, responsiveness, and customization of the experience. And this is where the digital twin can bring about a competitive advantage. Today the customer journey complexity is unprecedented, comprising not only the series of customer-facing interactions but also an enormous number of invisible-to-customer back-end activities: cross-functional collaboration, decision-making events, and compliance controls.  The end-to-end view of all operations involved in the service delivery is called a value stream. The optimization of the value streams and gaining business agility can be achieved through the operations digital twin enabling a continuous optimization cycle in the organization.
An operations digital twin for value stream is a simulation model connected to the data layer that replicates operations structure along with various systems, people, and even digital workers facilitating operations execution.
One might ask what the difference is between operations digital twin and BPMN solution. The answer is often on the surface. BPMN solutions are designed for automation purposes of a specific process, and they rarely span the entire end-to-end value stream involving cross-system flow orchestration. In contrast, operations digital twins are designed to augment decision-making for the end-to-end value stream optimization that might reside amid several systems. Operations digital twin helps you to see the entire flow, identify the flow constraints and bottlenecks, run simulations of various scenarios, and predict future performance.
Operations digital twin can benefit from insights of BPMN and even process mining solutions to capture the value stream structure and get specific data points describing the behavior of a particular step. It is a complementary solution solving different problems.
Another significant value for financial services embracing operations digital twins is the ability to enable continuous optimization cycles. Built once, a digital twin always reflects the state of the value stream and highlights performance issues, saving time for analysis and issue identification; plus, it accelerates the decision-making process, simulating the result of a specific decision or even recommending the next optimization improvement.
Digital twin for new policy evaluation
The financial services industry is quite regulated. There are specific company policies, local jurisdictions and international laws, local and global regulators covering different topics and often enforcing different policies from different organizational units; here are just a few examples:
- Financial security and compliance to prevent money laundering, tax offenses, fraud, sanctions, embargos, etc.
- Information security protecting customers from cyber risks, data protection, etc.
- Compliance department: enforcing regulatory requirements around processes, standards, etc.
Compliance functions are impacted by regulatory scrutiny beyond the ad hoc needs for additional capacity that arise during the remediation of regulatory findings. How should we evaluate the impact of the policy changes addressing a new regulation requirement on the services’ executions? Having operations digital twin describing the end-to-end value delivery (value stream) of the particular service, company management can explore different implementation options, evaluate the impact on the cost, headcount, and time to value of the particular service, as well as find the optimal solution so that company management can make an informed decision on how to roll out a policy change without disrupting the customer journey.
Digital twin of the organization
In a financial company, there are numerous customer interactions, operations, services, products, channels, systems, roles, applications, processes, and resources. These all act together in a network of business operations that could be represented as a network of operations digital twins. Gartner defines a digital twin of the organization as a dynamic software model of an organization that relies on operational and contextual data to understand how an organization operationalizes its business model, connects with its current state, responds to changes, deploys resources, and delivers customer value. Enterprise architects and technology innovators can prioritize, guide, plan, monitor, analyze and scale complex initiatives with a digital twin of the organization. 
Such a comprehensive model can unfold indirect and induced impacts on company performance from the connected or dependent value streams. Today, it is common for organizations to run parallel operations optimization initiatives. Therefore, the ability to understand how these initiatives impact each other and the business together becomes of paramount importance to allocate a budget wisely and gain better results from the optimization initiatives, avoiding potential pitfalls such as local optimizations or unexpected constraint creation.
In a time of disruption, as is now in the financial industry, the ability to innovate and operate outside the historical landscape, prepare for unprecedented events, invent new services offering, or optimize value streams making the customer journey smooth and fast, all become possible with operations digital twins.