“A significant trend [in the insurance industry] is the shortage of good quality, experienced claims professionals,” David Fineberg, Head of Claims at Generali UK, said in our latest report, The business case for AI in claims. “There is also a shift in the types of skills required. While line of business technical claims expertise is a must, the need to have both strong relationship building skills and capabilities in data and analytics are seen as gaps.”
As AI becomes more integrated into claims management, the role of the claims handler is evolving. AI enhances tasks like policy checking, document intake, and data classification, transforming daily responsibilities. No longer confined to time-consuming administrative tasks, claims handlers now have tools that allow them to focus on higher-value activities. By automating repetitive tasks, AI enables handlers to dedicate more time to complex cases that require human empathy and nuanced decision-making.
Our research report Customer experience: The claim handler’s perspective suggests that this is in line with what claim handlers want.
The top 3 most tedious aspects of claims handling are reviewing and processing claims documents, compliance and reports, and data entry.
When asked about the most common feedback they receive from customers regarding the claims process, 35% of claims handlers said feedback was positive. However, faster claims resolution, automation of the claims process, lower insurance premiums, and more support were identified by claims handlers as important factors that could improve customer satisfaction.
The research also showed that 58% of claims handlers are eager to develop new technological skills.
Here’s a closer look at how claims handling will change in the near future.
Claims filing
There’s a lot of information to process at the First Notification of Loss (FNOL) stage. Historically, claims handlers need to sift through the evidence manually, extracting key details, entering information, and cross-referencing with policy records. This process is time-consuming, often taking hours to complete, as they have to ensure every critical piece of evidence is accurately recorded.
With AI, FNOL has become much more efficient. AI processes claim documents by extracting essential data, summarising content, and flagging items that might need closer scrutiny. For example, AI can identify essential information such as dates, locations, damage descriptions, and the types of losses reported. This not only saves time but also improves the accuracy of initial intake, as AI consistently applies logic without human error or fatigue.
Additionally, AI can detect inconsistencies or potential issues that might require extra attention. For example, if an accident report includes an unusual set of circumstances or a high-value item, AI can flag it for further review, ensuring that complex cases are prioritised.
Claims handlers can then focus on cases that require empathy and expert judgement, especially in high-stakes situations where a policyholder is dealing with a major loss and needs reassurance.
Policy checking
Policy checking is traditionally another time-consuming task, requiring claims handlers to carefully cross-reference each claim with the specific policy details. This manual review was necessary for accurate claims outcomes, yet it can take considerable time, delaying the process for policyholders.
AI streamlines this process, making policy checking faster and more accurate. At Sprout.ai, we use a training dataset of over 4,000 claims, enhanced with synthetic data, to develop custom models that can check policies with an initial accuracy of 85%. This means that new customers immediately see faster results as AI quickly verifies policy terms, limits, and exclusions. The models can be fine-tuned to match each insurer’s unique policy language and structures, further improving accuracy over time.
With routine policy checks handled by AI, claims handlers are free to focus on more complex cases. Instead of spending time on repetitive verification tasks, they can bring their expertise to cases requiring in-depth judgement, such as claims with unusual circumstances or high emotional impact for the policyholder.
Intake processing
Intake processing is traditionally a challenging stage for claims handlers, often slowed by messy or unclear claim documents. They have to sift through these documents, identifying relevant information while ignoring outdated or unnecessary data, which can be tedious and prone to error.
AI streamlines this intake process with specialised models like strikethrough recognition and multi-document recognition and splitting.
Strikethrough recognition is especially useful for identifying text that has been marked out—often old or incorrect information. It ensures that only relevant data reaches handlers, avoiding clutter from unnecessary details. This means claims are processed with cleaner, more accurate information from the start.
Similarly, the multi-document recognition model handles instances where claimants submit multiple receipts or invoices in a single image. This model separates each document in the image, saving handlers from the time-consuming task of manually splitting and organising each item.
With AI taking on these repetitive tasks, claims handlers can focus on analysing the content itself rather than preparing it, speeding up the review process and allowing them to provide more accurate, efficient support to policyholders.
Classification
After addressing initial document clarity, AI helps further by indicating which claims need handler attention and which can be processed automatically.
Claims handlers can immediately see which claims are accurate and ready for processing versus those needing a more detailed review. For instance, with Sprout.ai, 89% of claims can be processed with 100% accuracy, enabling automatic approval or rejection where appropriate. Claims flagged as uncertain can then be prioritised for manual review, ensuring that claims handlers focus on cases that benefit from human oversight.
This improves workflow speed and boosts the quality of decision-making, allowing handlers to engage meaningfully with complex cases that require empathy and expertise.
Fraud detection
In addition to streamlining tasks, AI can detect subtle patterns or anomalies that might otherwise go unnoticed, such as repeated claims from the same individual or an unusual frequency of claims in a particular region. For instance, a claim that seems routine might, upon AI analysis, reveal indicators of fraud that would warrant further investigation. This is more important than ever, as 65 per cent of claims handlers have noticed an increase in fraudulent cases in the last year, and AI enabled fraud is on the rise.
Early detection allows handlers to focus their investigative efforts where they’re most needed, saving time and resources by targeting high-risk cases. Meanwhile, genuine claims can be processed more quickly, enhancing overall efficiency and reducing costs. By helping handlers pinpoint potential fraud, AI not only protects insurers but also improves the speed and accuracy of service for customers, ensuring that claims are handled fairly and effectively.
The evolving claim handler role: Beyond paperwork
With these new tools, the expectations placed on claims handlers are changing. No longer confined to paperwork, they will work more directly with customers and offer expert guidance. In cases of property damage from a natural disaster, for example, AI can manage initial data processing, enabling handlers to focus on addressing policyholders’ concerns and helping them navigate repairs or temporary housing options.
On a larger scale, AI also helps insurers address talent shortages. As fewer people enter the claims field, AI bridges resource gaps, enabling teams to remain productive without expanding headcount. For example, if a team is understaffed following a natural disaster, AI can process simpler claims while handlers focus on the most complex cases.
As AI increasingly manages routine cases, claims handlers could focus on developing specialised skills, such as analysing AI-driven insights for risk prevention. If AI reveals that specific areas face higher flood risks, for instance, handlers can work with underwriters to adjust policies and offer preventive advice to policyholders.
Looking ahead
As AI advances, the claims handler role becomes more strategic, customer-focused, and impactful. Freed from administrative tasks, handlers can focus on making informed decisions, delivering personalised service, and building stronger relationships.
Soon, routine processes like the First Notification of Loss (FNOL) could be nearly fully automated, leaving handlers to focus on exceptions and complex cases. Industry leaders predict that in three to five years, AI may handle straightforward claims end-to-end, with handlers stepping in only for cases requiring human judgment.
Ultimately, claims handlers will be bringing greater value, supporting policyholders with advanced technology and their own expertise.
