AI AUGMENTS, NOT REPLACES, HUMAN EXPERIENCE IN CLAIMS MANAGEMENT – PART 2

Decision-making is stressful. Combined with a lack of time, there is a possibility of missing critical information, asking for vague details, or even gathering inadequate information. Analysts processing such a huge number of claims could be tired or mentally exhausted, or there could be a one-off day when a wrong decision is made. All of these can happen to anyone and jeopardize the initial or FNOL stage, thereby impacting risk assessment, reserves management, and claims lifecycle, leading to complications and attorney intervention later in the claim.

Imagine if an ‘AI-led Cyber Claims Assistant’ can support claims analysts during the initial stages, where straightforward claims are accurately assessed and processed efficiently. In contrast, complex claims are flagged off based on past data patterns and information parsed from unstructured data like claims notes and police reports. Human file handlers will thus be freed to make intelligent decisions at half the time they generally take while leaving more mind space to assess the risks of complex claims and make decisions accordingly. The claims process thus becomes more streamlined, apparent from the get-go, allowing handlers to ensure policyholder satisfaction throughout the process.

How can insurers rightly apply AI to process work that humans currently do?

With AI understood to support the decision-making process, it is essential to note that AI engines like Charlee™ can best be described as the missing part of the puzzle applied to specific business requirements such as claims automation or litigation management to make sense of data which otherwise takes plenty of human-hours to decipher. The claims handler can then use this data to hasten the decision-making process.

For an AI engine to assist in claims management, especially at the FNOL phase, it must,

• Be able to understand structured and unstructured data from myriad claims data sources. Can the engine read the claimant’s policy to know what is covered, the difference between a windshield from a headlight, the possibility of body injuries with the type of collisions, claim date comparisons with prevalent weather patterns in the area at the time of the accident, or even whether a photo is relevant to the date of the accident or earlier. These connections between data points will help adjusters make accurate business decisions. This underlines the tool’s reliability and ability to defer to human expertise.
• Be built within ethics and insurance regulations to ensure carriers are held accountable during every stage of the claims management process. Ensuring that the data is factual or accurate, AI algorithms continually checked for accuracy of predictions, transparency in their functioning, and articulate and testable at all times are some of the regulations to ensure relevancy and limitation of bias that tends to creep in with human intervention.

Future-ready and recession-proof built around pre-trained insights, specifically in the insurance domain for various lines of property, business, and casualty insurance, which can be integrated seamlessly with existing claims management systems.