How Liberty Specialty Markets Uses Tech for Claims Risk Management

Once perceived as intricate, time-consuming, and on occasion, frustrating, insurance claims processing has undergone a substantial transformation with the integration of new technological solutions. From artificial intelligence (AI) to big data and automation, the insurance industry is witnessing a rapid evolution towards faster, more efficient, and transparent claims handling.

What Is Future Role of Claims Handler?

With the advent of technology, the role of a claims handler has rapidly evolved. To keep up, they need to embrace digital tools to provide the best experience for their customers.

For the last few years, the industry has been intensely focused on developing two key areas. Firstly, they are working to streamline the value chain through technological integration, encompassing setup and tech finance, and secondly, using data and analytics to measure the speed of claims processing, as well as obtaining real-time insights to enhance overall business operations. These processes, established correctly, will ensure that claims processing sees an overall increase in end-to-end efficiency.

These transitions cannot take place in isolation – the ease of transition is intertwined with the insurance sector’s collaboration with the financial industry. As the technology continues to evolve, staying ahead of the adoption curve will be paramount for insurers looking to provide a superior claims experience to their clients.

Use of Advanced Data Analytics

Data and analytics are integral components in measuring the speed of claims processing. The ability to access and analyze data in real-time means that insurers can manage funds more effectively, ensuring that there are sufficient resources on the balance sheet to meet claims obligations promptly. Technology also aids in loss fund management, allowing claims to be paid promptly after an event occurs.

Use of AI in Claims Processing

The integration of AI into claims processing has brought about significant advancements in the industry. AI systems can analyze the vast amounts of data embedded in the claims process, streamline processes through automating basic functions and complete repetitive tasks in seconds, leading to faster response times and less reliance on and constraint by human capacity. Advanced AI analytics sift through vast datasets, extracting valuable patterns and trends that facilitate more informed decision-making.

Managing data allows the industry to identify and monitor market trends and detect instances of fraud. Additionally, AI models can ultimately enable claims handlers to make faster decisions and reduce the time it takes to assess and settle claims.

Technology in Practice

For successful real-time analysis and awareness of emerging risks and trends, a consistent and high-quality foundational data set is required. To that end, Liberty Specialty Markets has been conducting cross-regional and cross-functional reviews of the 25 loss code groups available to claims handlers.

By collectively reviewing cause of loss codes (COLs), both the handlers capturing the data at the point of claim, and the actuaries, underwriters and portfolio managers using this data later downstream, have been able to provide a baseline for the understanding of claims with more granularity.

In short, previously the stories behind the claims had been too high level and did not allow for deep dives into the root cause of the claims.

Via this cross-regional and cross-functional review, handlers from all four regions of Liberty can look at the causes of loss, which they capture at the point of claim and then enhance the options available to them to develop more granular stories about the cause of loss.

Artificial Intelligence Tools

To expedite these historic clean-ups, Large Language Models have been employed. This means that documentation for up to 20 years’ worth of closed claims can be reviewed to detail the COL, providing a new, more granular version of the claims story. From “trend,” Liberty can begin to move into the arena of “predictive.” And thanks to artificial intelligence and BOTs, the foundational cleanup can be done more quickly and with a higher degree of consistency.

Fire claims, for example, can retrospectively be filtered and categorized into either accidental or arson. This is done by registering the root cause of an accident, for example, as a “short circuit”, “unattended heat works,” or “discarded cigarette.”

As a result of this claims review at Liberty, the options or granularity available to handlers has increased and Liberty is retrospectively redetermining the COLs on closed claims, which enables a retelling of the stories provided to internal customers to analyze trends that have not previously been understood.

In essence, Liberty is looking backward to go forward.

For example, if one insured has had 25 claims from discarded cigarettes over the past five years, Liberty will sit with the buyer at its renewal and share the granular data associated with these claims. At the same time, Liberty will work with the customer to implement a no-smoking policy across all its factories. Then when the next renewal comes, the data hopefully will show benefits to both the insured and Liberty – all provided by this granular analysis of claims trends.

By using the data this way, the insured is likely to stay with Liberty longer, while Liberty gains more risk avoidance insights.

All insurers capture and record the story of their claims in different way, using different names, but the data collected is basically the same. The differentiator for what Liberty has aimed to create is the granularity and consistency that has been embedded for its Risk Avoidance insights.

At the end of the day, all insurers handle fire claims, but many do not lock down the root cause of the fire to benefit the company and insureds.

Industry Collaboration

Of course, insurers have opportunities to learn from cumulative claims experiences collected by the Lloyd’s market and other industry bodies such as the Lloyd’s Market Association (LMA).

One good example is the work being done by Lloyd’s in its modernization project, Blueprint 2, which, once delivered, will benefit the industry by providing consistent, automated and expediated claims data.

Smaller buckets of collaboration are also available. The industry must also expand opportunities available to bring data quality and consistency to shared risks and claims histories for the benefit of insureds – in a similar vein to the LMA Cause Codes or Lloyd’s catastrophe codes.

Faster, more efficient, and transparent claims handling will be created when individual companies embrace the advanced technologies that are now available, while industry associations must continue to help analyze claims trends with their high-level viewpoints.

The future claims handler is no longer confined to traditional practices but now can be at the forefront of integrating AI, digital tools, and advanced analytics.