The insurance industry has been built on data since its inception. Meanwhile, several other industry sectors have proven that there’s a need for a fundamental shift in how data is used—a shift that’s going to have widespread implications for the future of North American insurers and their customers.
Insurers have been slower to adopt digital twins than their counterparts in other industries.
A digital twin is a virtual model of a physical object. It spans the object’s lifecycle and uses real-time data sent from sensors on the object to simulate the behavior and monitor operations.
Digital twin technology can replicate processes to collect data vital for future predictions. It’s like a bridge between the digital and physical worlds. A digital twin is a computer program that harnesses real-world data to create simulations and predict how these types of devices or services will perform.
According to Accenture research, only 25% of insurance executives are experimenting with the mirrored world and digital twin technologies, even though 87% agree that these technologies will be essential for collaborating in the ecosystem partnerships required for long-term success.
In the past insurers used insights based on historical data to inform their business decisions.
They now have opportunities to understand their customers and their business in a whole new way due to:
- The arrival of cloud computing.
- A proliferation of additional datasets thanks to instrumentation of assets.
- Real-time or streaming data and analytics.
What are digital twins and why should insurers care?
Digital twins are a virtual replication or mirror of a physical system. As a way to visualize and contextualize data from physical and virtual assets, they bridge physical operations and digital capabilities, and enable the sharing of information with ecosystem partners.
In the early days, digital twin models were confined in size and complexity, but the scale of them is growing rapidly now with the addition of AI and automation. They’re also increasingly requiring the use of sophisticated analytics.
Consider a ship transporting goods from one port to another. Telematics of nearby objects warns that the ship will soon be exposed to a tropical storm. The ship’s digital twin takes real data from all of the ship’s systems.
Thus, a simulation is performed that reflects the effects of the storm on the ship. Based on the simulation results, the captain decides to return to port. Similar logic could be applied to smart cars, smart houses, and so on (see about Life Insurance Digitalization).
Customers expect insurance companies to quickly adopt technological improvements and assure their assets.
How are digital twins used in other industries?
Outside of the insurance realm, digital twins are being linked together to create living models of whole factories, product lifecycles, supply chains, ports and cities. Companies are using them to understand supply chain predictability, worker safety, maintenance and repair costs, and as a risk-free playground for innovation.
For example, Unilever is working with Microsoft to develop intelligent twins of its factories so it can test potential operational changes and improve production efficiency and flexibility.
With the assistance of AI, companies can act on that data. They can:
- Respond dynamically to real-time information
- Ask “what-if” questions about possible future scenarios
- Design and test new products in the virtual world long before ever constructing them physically
When we questioned executives, 65% said that they expect their organization’s investment in intelligent digital twins to increase over the next three years.
I’m not surprised. I agree with my colleagues that intelligent digital twins are driving a step-change in how businesses operate, how they collaborate and how they innovate. And I believe insurers that choose not to take advantage of the many benefits of digital twins will struggle to participate in the markets and ecosystems of the future (see Why Digital Experiences are Essential for Insurers).
In the following posts, I’ll look at why insurers have been slower than their peers in other industries to adopt digital twins, four areas where you could leverage digital twins to your advantage and how you can put intelligent digital twins to work.
The Importance of Digital Twins in Insurance
Digital twins are computerized models or virtual replicas (simulations) of physical objects such as devices, assets, products, or processes. The biggest advantage of digital twins is that they operate in real-time, taking cues from updated data. This holds immense potential for the insurance industry, which heavily relies on data insights to make critical business decisions.
Digital twins leverage connected technologies (IoT, sensors, real-time data monitoring systems) to help insurers define new policies or packages, mitigate risks, reduce administration costs, and enhance core insurance operations such as underwriting, claims processing, new policy creation, fraud detection, customer assets evaluation, etc.
They also allow insurance companies to prevent unplanned downtimes and discover new growth opportunities.
These stats prove why digital twins are important for insurance companies going forward:
- Data Bridge Market Research estimates that by 2027, the global digital twin financial services and insurance market will account for USD 77,530.82.
- 87% of insurance executives agree that digital twins will strengthen their ability to collaborate in strategic partnership ecosystems, crucial for long-term success.
- 93% of insurance executives realize the necessity of a centralized and intelligent data hub that helps them understand the defects of their current processes and remodel their operations.
Digital twins can act as a virtual insurance lab for executives to predict and evaluate any risk scenario and make smart decisions (see about The Future of Digital Transformation in Insurance).
With digital twins, Insurance is moving to Assurance, a new business model that saves huge compensations from being paid off by averting losses before they occur.
Let’s consider the example of a cargo vessel that is ferrying goods from one port to another. Telematics is alerting the vessel’s crew that the ship will soon be exposed to a sea storm. The vessel’s digital twin can collect data from the entire ship’s systems and assess how the storm will impact the vessel. This enables the captain to quickly turn their route and reach the safest port nearby. This is a classic example of how digital twins help upgrade insurance into assurance (see about Insurance Digital Marketing & Sales).
There’s inertia around products and pricing
Using digital twin data, including streaming data and real-time risk data, means changing how products and offerings are priced. This goes against 200 years of actuarial sciences based on pooling data, assessing risk and building insurance products that insure the masses.
While we’ve seen a proliferation of usage-based products in personal lines auto over the last decade, with some carriers achieving meaningful scale, I think that scale is the exception and wonder how much of that captured telematics data is really finding its way into pricing algorithms.
Data platforms and data patterns are often too heterogeneous to provide meaningful insights
It takes a certain scale of homogenous data to be able to draw substantive conclusions. In personal lines auto, for example, if you pulled telemetry data from a Toyota black box, you might very well be able to make effective use of that data (see Insurers` Digital Strategies for Personal Customer Engagement). Because there are so many Toyotas on the road, you could draw broad conclusions from it.
In the world of personal transportation, the data volumes and behavioral attributes of that risk are quite homogeneous, so insurers can develop new products and pricing with confidence.
But for home insurers refining their offerings for connected homes, it could be more difficult. The types and maturity of instrumentation vary widely, as do the datasets, depending on whether you’re looking at data from Google maps, Amazon devices, ADP security systems, or the building management systems of commercial properties.
The same is true across the various industries that insurance carriers serve. Data payloads could vary wildly across public entities, transportation entities and manufacturing facilities for example.
Digital twins offer valuable opportunities
Despite these hurdles, I think the very real benefits of digital twins are worth the effort for insurers. More data from a range of sources paired with analytics and AI can offer a wealth of opportunities to reduce costs, grow revenue and provide customers with better service.
Where digital twins could help insurers?
Insurers, particularly those with personal lines, have already made good strides when it comes to using data to improve distribution. They’ve focused on creating a digital copy of the book of business and been building digital twins of customers based on their online activities, search habits and where they shop.
While this greater understanding the customer journey is helping insurers be more efficient and contextually relevant in selling, leading insurers are taking things a step further.
They’re looking to offer one-click purchases and an omnichannel ecosystem so customers can move seamlessly between channels.
But there are additional opportunities for insurers to improve how they service existing customers and for cross-selling. The more you know about the customer on the phone, the better you can provide targeted advice and services.
For example, imagine if you had digital twins not only for the customer but also for their insured assets and for external events affecting their decisions or their assets. You could pull these threads together into a much more comprehensive view and use that to provide a superior experience.
In underwriting, real-time streaming data could provide a more nuanced understanding of risk and improvements in pricing. Leading insurers are also looking to offer customers real-time risk prevention and risk-DNA-based insurance—in an effort to be a more holistic provider of protection for their customers, not just a business that pays you when something bad happens.
For commercial lines insurers, while getting a more homogenous data set across customers may be a challenge, I believe the benefits will be worth it.
We’ve seen it already for workers’ compensation, with payroll and ERP integration helping carriers to understand the changing workforce, associated premium changes and reducing the need for premium audits.
I recommend commercial insurers explore this in the terms of coverage (property, liability) and sector (energy, construction, marine).
Operations in Insurance
Within the four walls of an insurance carrier, there’s a material opportunity to drive efficiency and enhance decision-making by adding connected instruments throughout the enterprise.
Now is the time to turn attention to the more traditional core insurance functions of distribution support, underwriting, claims and back-office administration functions.
With building a digital twin of the operation, two streams of benefit quickly start to emerge:
- Automation opportunities come with the traditional analysis of keystrokes and process steps.
- Intelligence opportunities come when you look at how to use that data differently. This is likely where the larger value comes from.
As an example, imagine if you could take a digital mailroom capability that scans and stores digital copies of physical documents and turn it into something that fundamentally triages and automates work differently based on the type of document and where that customer is in the transaction process.
Insurance Claims Processing
Digital twins help fast-track claims processing by reproducing the scenarios or circumstances behind the appeals, such as conditions of damage, car accidents, fire breakouts, etc.
Claim adjusters can leverage digital twins to simulate accidents and assess their impact on the claimant’s property or other valuable possessions.
This avoids the need for expert examination and helps precisely determine the insurance company’s liability. To prevent unwanted delays in claims processing, executives can compare the virtual and physical records of an accident or a house fire. Thus, digital twins in claims processing allow insurers to respond quickly to their customers.
More efficient and faster claims processing is another area where I see tremendous opportunities for taking advantage of digital twins.
We’re already noticing leading insurers seeking real-time coverage optimization, integrated restoration infrastructure and the use of human + AI to improve the employee and customer experience.
For example, imagine that you offer personalized auto insurance and one of your customers is involved in an accident.
Think about the insights you could gain if had access to:
- Telemetry data indicates what was happening with the car at the time of the accident—the way the wheels were turning, the speed of the car and how hard the driver was braking.
- Weather patterns that explain the driving conditions when the accident occurred.
- Information on the driver such as how well they slept last night and whether they were distracted by something they saw or by talking with a passenger.
- Service data such as how long it will take to get the car repaired, how long the wait time is for the parts you need and whether you are connected with your own service fulfillment supply chain through your network partners.
The more you delve into the details, the more possibilities you can see to take advantage of digital twin data across the insurance business.
FBI reports that in the United States, the total cost of non-health insurance fraud alone accounts for USD 40 billion per year. The rising insurance fraud costs force carriers to increase their premiums, which has a direct bearing on many American families who will have to pay higher premiums every year.
Thanks to digital twins, it’s now possible to reproduce an event that resulted in a catastrophe or damage.
Claim adjusters can determine the claim’s accuracy by comparing the applicant’s data with the simulated version of the incident. As a result, inconsistent claims can be detected quickly to reduce the carrier’s liability and save costs.
Like HR, Finance, Procurement, Inventory, and other corporate functions that can be augmented with automation, several back-office operations in insurance companies can be improved using digital twins.
It’s possible to create the virtual simulation of a blockchain-based smart contract prior to implementing the actual contract.
Insurance carriers, customers, claim adjusters, and relevant policy stakeholders can explore how the smart contract would work in real-life and ascertain how secure the contract would be.
While blockchain ensures transparency and trusted execution of smart contracts, digital twin stores and evaluates the insurance data in real-time. This helps avoid any legal disputes and complications in the future.
AUTHOR: Jim Bramblet – Senior Managing Director, North America Insurance Practice Lead at Accenture, Vinod Saratchandran – Project Coordinators & Analysts at Fingent