Viewpoint: Gen AI Offers a New Frontier in Claims Modernization

When it comes to modernization, it sometimes feels like the insurance industry is running to stand still.

In recent years, carriers have invested significantly in intelligent automation, standardizing workflows and guiding processes like claims triage. They have also spent substantial time and money compiling a library of claims communication templates for use with parties, including lawyers, repairers, and policyholders.

The investments to date have vastly improved the efficiency and consistency of claims services and allowed claims specialists to spend more time on higher order work. However, the advent of generative AI means that what were certainly smart investments at the time could become obsolete, unless carriers embrace the potential of this transformative technology and integrate it into what they’ve just built.

Existing systems suffer from an inability to adapt and innovate based on changing claims patterns. For example, rigid claim triage rules, which are reviewed infrequently, can cause teams to be underutilised or overwhelmed with irrelevant cases.

Claims documentation templates, meanwhile, may save time when dealing with straightforward claims but are rigid, inflexible, and unable tooffer the personalization that gen AI facilitates.

Gen AI systems powered by large language models, by contrast, have no such limitations, given their ability to adapt, learn and innovate at speed, drawing on vast quantities of structured and unstructured data. When working alongside business rules engines, gen AI can spot patterns in decades of claims data almost instantly.

This immense analytical power makes claims – once a highly unpredictable source of losses – eminently more predictable, and arms human claims experts with knowledge stretching back way beyond their professional experience. Gen AI can then “super-analyze” claims data and provide insights on portfolio performance and pricing to be shared across the organization. The long-term trends it can identify then become actionable.

In terms of operating efficiencies, gen AI is a compelling proposition. Insurers have always attempted to strike a balance between the size and cost of their claims operation on one hand and claims efficiency on the other.

However, gen AI gives insurers another lever, allowing them to increase the amount of work product they deliver without needing to increase the human element. Key opportunities include reading claims correspondence and the associated attachments, ingesting the data, spotting what’s missing, and even establishing a reserve. All this frees up specialists to provide added value services.

Fraud Detection and Prevention

Importantly, gen AI also turbocharges carriers’ ability to spot fraud, since high-quality data is essential for effective fraud detection and prevention. Inaccurate data, by contrast, can lead to missed fraud indicators, resulting in increased fraudulent claims.

The technology’s role here is one of its most enthusiastically adopted use cases. It looks set to become increasingly important as the insurance market starts to soften and if economic malaise increases, with the inevitable uptick in crime.

In terms of customer experience, claims digitization is already helping optimize the journey, particularly with regard to the first notice of loss experience. But what comes after that, and how it affects the everyday experience of policyholders with claims, whether they’re looking for updates, chasing the progress of a repair, or needing an additional service such as a child car seat for their rental vehicle, is where forward-thinking insurers can really differentiate themselves.

Gen AI can provide for hyper-personalization and do away with the need for communication via a set of rigid templates. Deployed well, the technology can elevate the overall claims experience towards what you might expect from carriers working with high-net-worth individuals and SMEs, without the associated cost base.

Next Steps

Insurers that are already weary of the modernization treadmill should not lose heart.

In the arena of claims, carriers that have cleansed their data to ensure its accuracy and consistency and that are recording it in one place, accessible to all who need it, are well positioned. So, too, are those carriers that have invested in a modern claims system from a reputable software provider over the last few years.

They have laid the building blocks for gen AI to unlock opportunities that were either unachievable or too expensive before. It is time now to allow gen AI to optimize that investment and make data productive.

Of course, implementing gen AI successfully requires careful change management. While the c-suite may be uniformly in favor, modernization fatigue lower down the organization, particularly among middle management, can easily set in. Cultural resistance among staff who may perceive gen AI as a threat, or who are unwilling to change the way they’ve been working for years, is another obstacle to overcome. Over-zealous procurement experts can also kill a project dead.

It’s also critical that re/insurers evaluate gen AI and its performance, whether in claims or in other areas of their operations, at regular intervals. Concrete metrics such as response times and user satisfaction scores are most effective here. Governance should be clear and understood across the organization, and the potential risks, which include regulatory issues, algorithmic bias, and AI-generated “hallucinations,” should be evaluated.

Incorporating gen AI isn’t a once-and-done exercise but is more akin to identifying a new muscle and learning how to use it. Figuring out how to integrate AI is vital in ensuring carriers protect their existing claims investment.

In the long term, using gen AI for administrative and analytical functions within the claims department, and even for decision making, is less dangerous than working with obsolete systems and processes. The rewards are substantial, while the risks of doing nothing include a permanent loss of relevance to consumers, and a financial performance that lags behind all but your most antediluvian competitors.

Topics InsurTech Claims Data Driven Artificial Intelligence