Insurers rely heavily on structured data—policy dates, claim amounts, customer demographics—to assess risk and detect fraud. But some of the most valuable information is hidden in unstructured data, like adjuster notes, free-text fields, and intake documents. The challenge is that this information is lengthy, inconsistent, and difficult for humans to parse. Shift’s AI solves this problem by analyzing unstructured text at scale, uncovering suspicious details that would otherwise go unnoticed and helping insurers make faster, more accurate decisions.
In this case, Shift’s AI demonstrated its ability to unlock hidden value in unstructured data. Claims are filled with free-text notes from adjusters, call centers, and investigators, providing information that is vital but often too lengthy and complex for human reviewers to analyze thoroughly. By applying advanced natural language processing, Shift’s AI was able to read through all of the notes, identify suspicious elements, and connect them with other claim data to reveal a clear fraud risk.
In this case, AI surfaced the following critical red flags:
This level of insight simply wouldn’t have been possible manually. The insurer gained the ability to see beyond structured data fields and use all available information to make better decisions. Ultimately, Shift’s AI not only prevented a costly fraudulent payout but also proved how unstructured data can be transformed into a powerful asset for fighting fraud.
This video is part of a series of interesting cases presented by our insurance-focused team of data scientists. For more examples of Shift Technology's AI-driven results, browse the AI in Action library