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.