Consider a jigsaw puzzle with pieces you must find and put together. The task is easy and can be finished quickly if it’s a small puzzle of less than ten pieces. But consider a puzzle of less than 100 pieces. Besides understanding the picture and connecting all the pieces, it also becomes essential not to lose the relevance of one puzzle piece while trying to find and join the others.
A seasoned claims adjuster handed a stack of claims files is going through the process of putting together the puzzle with the pieces provided. Each claim file is filled with lengthy, dense texts that include accident reports, notes and scribbles, medical records, and unstructured text data. The adjuster has to manually sift through this entire trove of information to identify critical details, note discrepancies in claims statements, go through past claims records, spot inconsistencies, and determine the claim’s risk, stage of complexity, and validity. Straightforward claims are easy to manage and resolve. But claims with hundreds of notes, vague or haphazard data points, and gaps in narration can be mentally taxing and time intensive, with some crucial information falling through the cracks that may or may not be grasped till much later.
Now, picture all the above claims adjuster tasks being performed with the assistance of an insurance-trained Large Language Model (LLM) at a speed and scale much beyond human capacity, at a fraction of the cost, and with ten times the accuracy.
Given the humungous amounts of text data generated during the claims process, LLMs can significantly transform the claims management process. By doing the heavy lifting and taking over large parts of workload processing, AI models can optimize and expedite the claims process, increase accuracy, bring down costs, and substantially minimize the operational burden.