During a major catastrophe, both the insurer and the insured want the same thing-to process property damage-related claims faster, efficiently and accurately. Insurance companies take the help of claims adjusters to inspect the damaged property and prepare an accurate loss estimation report. The adjusters use advanced estimation tools such as Xactimate to prepare the loss reports on the basis of gathered loss information and sketches/images of the structure, which then gets reviewed by the quality assurance (QA) team before getting submitted to insurers. But, even a small human error during report preparation can lead to multiple reviews by the QA team, site revisits, disagreements between insurers and claimants and also inaccurate claims settlements. So, how can one ensure accurate claims estimation?
Damco’s Machine Learning (ML)-powered-solution validates the loss estimation report using the tools adjusters use and identifies the errors in minutes. It helps adjusters and carriers prepare a fast and accurate loss estimate report which is critical to processing a claim efficiently while reducing the claims handling cost and unnecessary rework. The solution solves the following challenges for carriers and adjusters:
The Advanced Analytics (AI)-based solution leverages Optical Character Recognition (OCR) tools and Machine Learning (ML) models to analyze the images captured in the loss estimation reports. It can feed in different sets of QA rules and checks to detect errors as per your requirements.
Damco helped a leading US-based independent insurance adjusting firm eliminate human errors in loss reports, increase the productivity of QA teams and improve customer experience with faster claims settlements.
The client was facing challenges with its loss estimation reports that lacked accuracy with respect to the identification of house roof material (Hip, Gable, and Shed) and type of roof shingles. This led to multiple checks by the Quality Assurance team before the report was submitted to the insurance carrier. Know more
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