Why using a 'digital twin' could impact insurance claims, prevent fraud

Imagine a ship. Once it is built and placed into service, the vessel will require maintenance and repair. Inclement weather and natural disasters may impact travel and the delivery of its merchandise. 

Is it possible for an insurer to help prevent damages and delays? Yes. 

An insurance company that chooses to underwrite the risk can rely on a digital twin—a virtual representation that provides a real-time digital counterpart of a physical object or process. Digital twins will allow insurers to focus on monitoring the risk associated with the vessel rather than pay for repairs and replacements once an undue event happens. The term 'digital twins' was coined two decades ago by Michael Grieves. Industries—such as construction, fleet management, and manufacturing—embraced it. The opportunity for the application of digital twins in insurance is now clear. 

For insurers, digital twins can provide data to take preventive steps that optimize operations and minimize losses. Digital twins of various types can advance insurance innovation across sectors by virtualizing everything from driving habits and digital fleets to smart wearables and smoke alarms. These information models can help inform decision-making that strengthens customer relationships and refines pricing and premiums.

Historically, insurers relied on data available from limited interactions with customers, most frequently at the touch points created when a product is purchased or when a claim is made. Today, historical data simply is not enough. 

Fortunately, technological advances are allowing insurers to rely on real-time data from digital twins. The internet of things is a primary force behind the use of digital twins, as the IoT collects and provides a large amount of data from a wide range of devices. Other technologies that are advancing the use of digital twins in insurance include cloud computing, which supports the compute and storage needs of digital twins; artificial intelligence, which can help extract important insights from real-time data; virtual reality, which can render the data in two- and three-dimensional space; application program interfaces, which allow quick and reliable access to data; and 5G cellular networks, which speed and secure connectivity between IoT devices.

By providing new methods of contextualizing and visualizing data, digital twins help insurers avert disasters and minimize damages. They can also help insurers optimize core business operations, including claim management, customer engagement, fraud detection, product innovation, and underwriting. Digital twins enable scenario analysis, offering the potential to strengthen compliance initiatives and risk transfer/prevention. 

Use cases for digital twins within the insurance industry are robust. By providing real-time data for increasingly complex risks (i.e., from natural disasters' impact on physical assets, the digital economy's challenges from cybersecurity risks, or damage possible from climate change), digital twins offer the opportunity for insurers to create value across product lines and insurance sectors. 

In the home insurance sector, for example, the insurtech Hippo leverages home security systems from Notion, Ring, and SimpliSafe to monitor carbon monoxide, motion, smoke, and water leaks. In auto insurance, for example, driver and car parameters can indicate predictive maintenance and reduce fraudulent claims. In health insurance, smart wearables can monitor health parameters to provide warnings when an anomaly is detected and can call emergency services when needed. In the industrial sector, embedded sensors within infrastructure or wearables for workers can deliver cost savings and minimize the risk of fraudulent claims. Other possible applications (as illustrated in the graphic below) exist across coverage areas, including auto, cyber, fleet, health, home and buildings, and industrial.

Applications and advantages

Digital twins can be applied to multiple business processes of an insurance company, including, but not limited to: 

• Claims processing, where digital twins can simulate an accident. This data can illustrate the impact on property, which both speeds the processing of the claim and can retrain machine learning algorithms. 
• Fraud detection, where the digital twin holds data on a property and the reported damages, shedding light on inconsistent claims. 
• Underwriting, where insurers can leverage expanded data sets and run simulations that improve the underwriting process to offer more competitive premiums to customers and prospects. 

The possible advantages of using digital twins in insurance are substantial. The insurer may benefit primarily through reduced costs. These savings come both through predictive maintenance and warning services, where repairing a potential cause of damage (such a leaking pipe) is far less expensive than repairing the eventual results (such as extensive water damage), which would be a much larger claim. Savings are also possible by using the digital twin to monitor an insured item, providing clarity into when claims occur and preventing fraud. 
Insurers also benefit from lower risk. Digital twins improve the accuracy of risk assessments, as they provide additional data about a customer. The robust data analysis available through a digital twin also provides greater insights into insured items, indicating demand for products and services. Insurers may use this data to identify new revenue resources, perhaps through collaboration with third-party providers and suppliers. 

Digital twins can also help improve the customer experience. By facilitating communication about things like recommendations and warnings, digital twins can enhance engagement, improve customer loyalty, and reduce the customer churn rate. 

Implementation 

Insurers will need to move beyond challenges in order to implement the concept of digital twins and to ensure that the investment in this technology is merited. Insurers may need to make a concerted effort to get customers to consent (opt-in) to having their data used in this capacity. Legacy IT systems, including data management and warehouse platforms, may need to be redesigned to take full advantage of the real-time data offered by IoT devices. Depending on specifics related to each sector, insurers will need to address privacy and security issues. Intellectual property and cybersecurity must be protected, as digital twins hold the IP and sensitive customer data of the original product.

Yet use cases for digital twins are limitless, meriting consideration and implementation. Insurers may start small, and then expand. To reach the goal of an operable digital twin, begin by identifying use cases, then design the digital twin, build a minimum viable product (MVP), and build out the digital twin. 

Because digital twins are only as reliable as their data, it is essential to maintain data integrity, which must be done cost-effectively. Data must be granular enough to accurately represent the elements required. Data governance, for interoperability, is required and exchange formats must be considered. Data must maintain its relevance, so the challenges of legacy data must be addressed. Finally, because data democratization is essential, the digital twin's data must be easily accessible. 

Once fully implemented, a digital twin provides real-world benefits for the real-world entity that it doubles. The upsides for insurers include innovation, streamlined decision-making, improved processes, sustainability, resilience  and value generation.