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Article | Insurer Insights

The road to driverless claims processing

By Tom Helm | February 10, 2020

Insurance claims is forging its own digital transformation path, leveraging advancements in technology and analytics to inject automation into the claim process. So how far away are we from delivering claim processing that is automated end-to-end?
Insurance Consulting and Technology
Insurer Solutions|

Whether you envisage that self-driving vehicles are five years away or 25 years away, it is hard to argue, given the substantial investments and technological progress being made, that it’s anything other than on track to happen in some form at some point. Not to be outdone in this digital age, insurance claims is forging its own digital transformation path, leveraging advancements in technology and analytics to inject automation into the claim process. So how far away are we from delivering claim processing that is automated end-to-end? Will this happen before driverless Ubers are roaming the streets?

The short answer is “Yes.” But, no doubt like the first “driverless” cars that will come to market, there are caveats.

“Claim processing” covers a wide range of activities and claim types from the simple, like the loss of a low-value specified item, through to the highly complex, like the evaluation of a business interruption event or assessing negligence in a professional indemnity claim. As a result, the pace of digital transformation across the full claim spectrum will vary, with certain elements feeling like revolution and others evolution. But ultimately digitalization will touch every corner of the claim world.

Next generation claims

Automated claim processing is not completely new. Many insurers have been processing high-volume, low-cost, low complexity claims, such as vehicle windshield claims, with very little, if any, human intervention for decades. This is a great example of the simplest type of claim to automate given that it is relatively low value and that the fulfilment is being delivered by a trusted supplier partner within an automated solution and predefined parameters.

A natural quick-win expansion of this approach has been to target similar simple claim types, or individual losses or items that are part of a wider claim, that lend themselves well to automation. Good hunting ground is low-value retail property and travel claims. However, this does require expanding claim automation to cases where the settlement (payment) is going directly to the customer, and this means pushing the historical boundaries, particularly in relation to trust. Given the historical level of fraud and other market dynamics that find customers increasingly switching providers, thus reducing their historical data footprint (record of trust), implementation here needs to be executed expertly to mitigate against these risks. The appetite to broaden this automation also highlights why behavioral analytics, which can support the assessment process, will play an ever-increasing role in the future of claim processing.

Perhaps surprisingly for some, claim processing for one of insurance’s highest claim volume products, auto insurance, falls into the more “complex to automate” category (for the non-windshield cases), particularly when considering end-to-end automation. One auto claim can often represent several mini-claims rolled into one, with multiple parties, suppliers and a variety of claim types (i.e., vehicle damage, car rental, injury) all to be managed. Claim InsurTech firms have recognized this challenge, and rather than attempting to bite off too much at once, they have typically focused on disrupting distinct elements of the auto insurance claim process, with a significant number targeting digitization of the First Notification of Loss (FNOL) process, for example, using AI to assess images to determine the extent of vehicle damage or developing an e-FNOL solution that enables the customer, or his or her broker, to self-serve this part of the process through a digital channel. As these InsurTech providers reach critical mass with their products, they will inevitably seek to broaden their offering either by expanding to cover additional components of the auto claims life cycle or by diversifying into other product lines.

Of course, not all of today’s technological development is shiny and overt. A good example of this in the wider technology landscape is the Global Positioning System (GPS). While this operates primarily in the background of many of the devices and businesses we use, history will undoubtedly reflect that it has been one of the most transformational technological developments of our time, given its impact on our everyday lives. A parallel can be drawn here on where a substantial element of the claim technological development is taking place.

A lot of claim processing, across all product lines, is carried out manually and somewhat “behind the scenes.” Whether this is triaging, routing, validating, assessing liability, corresponding with third parties or evaluating an individual claim’s cost, there are numerous activities that are performed during the claim life cycle that are vital but often not visible outside of the Claims function.

Insurers’ increased focus on customer-centricity will lead to greater transparency, control and personalization for the end consumer.

Insurers’ increased focus on customer-centricity will lead to greater transparency, control and personalization for the end consumer and inevitably push some of these customer-related activities more into the limelight. For example, when their items have been lost or damaged customers are likely to be given greater choice on whether they wish to opt for a cash settlement or a replacement, and in many cases this will be via a digital interaction. This shift in experience will add to customers growing demand for more immediacy and effectiveness in the processing of these activities, akin to their experiences with other digital services.

Achieving full claims automation

A common thread across these activities is decision making, and this is where the technology opportunity kicks in. Computer science can play a key role in this decision-making process, and progressive insurers are already fully active in leveraging its predictive power. Supported by advancements in Natural Language Processing (NLP) with its ability to tap into unstructured claim data, AI can help drive increased accuracy and speed of decision making and in turn expedite proactivity in claim handling and thus help to deliver on traits that are synonymous with high-performing claims functions as they result in improved efficiency and significant financial savings, as well as delivering better outcomes for customers.

At Willis Towers Watson, we are helping insurers to deliver these next generation decision capabilities. Our clients are increasingly expanding the use of our suite of analytical software and real-time decision engine technology, Radar Live. As it is software they are familiar with and that is already integrated into their critical business decision-making process, by serving as their Pricing engine, they are recognizing that the product is agnostic to which insurance function it is supporting, and its use can be broadened. Essentially the software is an analytical canvas, tailored for insurance, on which multiple sophisticated machine learning models can be created or imported (e.g., from open source software) and with its ability to integrate to existing claim systems and provide sub-second responses, it is able to operate as a real-time decision engine for claim processing. Building the algorithms on a platform like this provides a road map for machine learning implementation from experimenting with models in a proof-of-concept environment, to refining the results, all the way through to fully operationalizing them and deploying them at scale.

Like GPS, software such as Radar can work in the background to provide decision making at key junctures in the claim life cycle. This might be selecting which supplier is best suited for the claim, determining the appropriate case estimate or assessing if an invoice is suitable for payment. Models can also be continuously running on the engine scanning to help alert the Claims team for cases that are “at risk,” for example, of issues such as fraud or litigation, or of delivering a poor customer experience. The power of machine learning is being leveraged throughout with the models trained to look across a vast array of structured and unstructured data to identify the characteristics of a claim and to assess the optimum response.

How the output of these sophisticated solutions is used will vary depending on the specific-use case it is designed to support. It might be to help fully or partly automate a claim process, for example, working in conjunction with robotic process automation to provide the response when a complex decision is required, or it could be to provide the claim handler with guidance on the next-best action.

The caveat to achieving full automation in claims is that certain aspects of the process or scenarios will require complex judgment, investigation or the human touch, such as the need to reassure and empathize with a customer who needs support during a significant event like a flood in his or her property. This means that claim handlers will need to remain in the driver’s seat to take control and handle these critical elements, and, much like partial self-driving vehicles, it is critical that automated mechanisms are able to identify when the situation requires human intervention and manage the interaction between handler and machine effectively.

The key task for claims leaders…will be in mastering the effective integration of technologies and analytical advancements.”

Tom Helm
Head of Claims Consulting,
Insurance Consulting and Technology

The ultimate success to self-driving vehicles becoming mainstream will be dependent on the experts achieving real-time orchestration of sensors, cameras, on-board computers and algorithms, interacting these technologies harmoniously with their external environment and determining whether there are certain scenarios when driver control is necessary. Comparably, the key task for claim leaders over the next few years will be in mastering the effective integration of the multiple technologies and analytical advancements that are now at their disposal to deliver seamless automated claim processing and decision support for claim handlers in a way that improves the experience for customers and realizes return on the investment for the business.


Tom Helm
Head of Claims Consulting 
Insurance Consulting and Technology

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