For leading insurers, the use of artificial intelligence (AI) in claims processing is a powerful tool for delivering on policyholder expectations and driving operational efficiency. By leveraging insurance-grade AI for claims processing, insurers are successfully mitigating some of the industry's biggest threats to long-term profitability.
The power of AI in claims processing
How are insurers using AI for claims processing?
AI for insurance claims is being used to streamline and improve the claims process using techniques such as machine learning, predictive analytics, and generative AI (GenAI). These technologies enable quicker and more precise claim resolutions than other methods, creating a better claims experience for policyholders while driving operational efficiency.
In practice, an AI-based claims processing solution quickly and accurately reviews claims documents within the context of other available data, then generates a summary of its findings alongside a recommendation for moving the claim forward. And with the emergence of insurance-grade AI, the most sophisticated models are trained specifically for the insurance industry, resulting in accuracy that matches or surpasses that of an experienced professional.
Summary of benefits offered by AI in claims processing:
- Greater operational efficiency
Claims handlers spend about 30% of their time on low value work, such as reviewing documents, and claims AI can minimise this. Seasoned professionals can spend more time on higher value activities, while less experienced professionals can use the tools to be more effective. - Improved claims experience for policyholders
An industry report found that 31% of policyholders who made recent claims were dissatisfied with their experiences, with 60% citing settlement speed as a cause of their dissatisfaction. The use of AI in claims processing accelerates resolution, in some cases from weeks to minutes, with highly accurate results. - Improved accuracy
Claims AI can quickly identify connections among vast amounts of data, drawing connections that may otherwise go undetected. For example, while photos may provide evidence of damage in an isolated claim, whether or not those photos were reused is virtually impossible to detect in manual review. Machine learning can score photos based on similarity to identify potential photo reuse.
The limits of rules-based claims automation
A "rules-based" claims automation system processes claims by adhering to set rules, while an "AI-based" system is highly adaptive, leveraging multiple AI technologies to interpret data, understand intricate situations, generate summaries and recommendations, and learn over time.
Why rules-based automation is insufficient for processing insurance claims
The vast majority of claims data is unstructured, and typical rules-based automation is insufficient in handling this. This is highlighted by the fact that while many insurers aim to implement claims automation, only 7% of claims can be ingested via straight-through processing. Documents such as police reports and handwritten notes do not always adhere to standard formats, and even when they do, they may not be filled out correctly. Additionally, while a rules-based decision engine may be able to interpret some of these documents, it’s not capable of placing them into context. This requires manual intervention to assess the output and determine the next step, inevitably slowing the claim process and potentially introducing inconsistency.
How AI in claims processing is solving the challenges of automation
On the other hand, the use of AI in claims processing excels at analysing these types of unstructured data, then goes one step further to drive towards the next steps in the claims process. This is achieved by going beyond optical character recognition (OCR), and employing statistical models that are trained specifically for reading and analysing insurance documents. With unstructured data sources now integrated to the structured claims data, AI can assess and recommend decisions for claims teams. This not only accelerates a resolution, but also leads to more consistent results and greater accuracy.
Examples of AI in insurance claims processing
The following examples feature real results from the implementation of claims AI technology. Watch the videos to hear Shift's data scientists present the details of each case.
Example 1: Travel insurer achieves 57% automation while reducing processing time from weeks to minutes
A large US-based travel insurance company, handling 400,000 claims per year, had an average claims processing time of up to three weeks. All claims were being handled manually with 0% automation. They deployed an AI-based solution that transformed the manual process into a streamlined, automated experience, achieving 57% automation and reducing processing time from weeks to minutes.
Video: Shift data scientist discusses how AI drove these results
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Manage CookiesExample 2: AI in claims helps top 5 insurer uncover hidden subrogation opportunity
Through the use of advanced GenAI models, this property & casualty insurer received a subrogation alert in a state that other methods would have typically overlooked due to stringent legal criteria. These advanced AI models were able to ingest the claim and quickly evaluate it against all available data to determine that it did meet local criteria for subrogation. Insurance-grade AI for claims not only streamlined the evaluation process but also ensured accurate identification of fault, ultimately saving valuable time and enhancing recovery opportunities.
Video: Shift data scientist presents an example of how sophisticated claims AI technology can maximise recovery potential
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Manage CookiesExample 3: Photo similarity scoring quickly finds connection overlooked by other methods
An insured individual made a claim for damages related to inclement weather. In his claim, he submitted photos showing damage to his refrigerator and freezer, as well as photos depicting spoiled food. While the photos appeared to provide evidence supporting the claim, claims AI flagged them for similarities with photos used in three previous claims. No payment was made, and the insurer was able to stop payout quickly and prove fraud with simple desktop investigations.
Video: Shift data scientist provides detail on the case and how claims AI revealed collusion between multiple fraudsters
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Manage CookiesAdditional resources
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- Browse the Shift resources library, featuring reports, webinars, perspectives, and videos
- Contact us to learn how Shift can help you successfully leverage AI in claims as well as the entire policy lifecycle
