For detecting suspicious claims, manual review alone is often impractical
For insurers, reviewing a policyholder’s claim history to detect suspicious patterns is tedious, time-consuming, and often impractical at scale. Fraudulent activity can hide in overlapping claims, repeated losses, or subtle misrepresentations of facts. Shift’s AI helps insurers automatically analyze policy and claim histories, uncovering patterns that humans would struggle to detect and enabling faster, more accurate decision-making.
AI leverages policy history to detect complex fraud
Detecting overlapping claims with manual review is challenging. Reviewing prior losses with this method is time-consuming and prone to human error, making it difficult to catch subtle patterns across multiple claims.
Shift’s AI analyzes policy and claim histories at scale, uncovering patterns that indicate potential fraud. By comparing the circumstances of the current claim with prior claims, AI recognizes overlapping patterns and flags cases for further investigation.
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Overlapping claims identified automatically, eliminating tedious manual reviews
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Investigations triggered quickly and automatically, enabling fast, precise action
AI detects overlapping claims, saving insurer $525,000
In the video below, Brian Savage, Senior Data Scientist, walks us through a recent case involving a kitchen fire claim valued at approximately $525,000. At first glance, it appeared to be a new, isolated loss. But using Shift’s entity resolution techniques, we were able to reconstruct the policyholder’s history and identify a similar claim from two years prior. The AI detected significant overlap between the circumstances of the past and current losses, prompting an alert to the insurer’s Special Investigations Unit. Upon investigation, the insurer determined that the current claim facts were being misrepresented, ultimately denying the payout and saving $525,000.
How AI leverages policy history to detect complex fraud
Shift’s AI leverages multiple advanced techniques to uncover fraud that would be extremely difficult to detect manually. In this case, the system analyzed the policyholder’s entire claim history and used entity resolution to reconstruct past claims. By comparing details across multiple claims, including dates, circumstances, claimant information, as well as patterns of loss, the AI identified a prior claim from two years earlier that significantly overlapped with the current kitchen fire loss.
The system then applied sophisticated pattern-matching and anomaly detection, connecting subtle similarities and inconsistencies that human reviewers would likely miss. This analysis generated a high-confidence alert to the insurer’s Special Investigations Unit, highlighting potential misrepresentations in the claim.
By automating the review of historical claims and cross-referencing them with incoming losses, Shift’s AI dramatically accelerates detection, reduces manual effort, and uncovers complex fraudulent patterns. In this case, it enabled the insurer to deny a $525,000 claim, demonstrating the power of AI to turn unstructured and structured data into actionable insights.
Learn more from our team of 200+ data scientists
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
