Why traditional entity resolution falls short
Traditional entity resolution—the process of identifying and linking records that refer to the same individual, vehicle, or organization across different datasets—can be slow, error-prone, and limited in scope. Manual matching or basic rules-based systems often struggle with inconsistencies such as misspellings, incomplete data, or variations in reporting, making it difficult to accurately connect people and claims. Shift’s AI, in contrast, uses advanced algorithms and large-scale pattern recognition to intelligently reconcile entities across multiple sources, uncovering hidden relationships and detecting complex fraud scenarios that traditional methods frequently miss.
Solving the challenges of entity resolution at scale
Traditional reconstruction methods are limited, making it difficult for auto insurers to link people and vehicles across past claims.
Shift's AI models are trained specifically for this type of reconstruction, leveraging thousands of intelligent rules and advanced data-linking techniques to accurately reconstruct people and vehicles across multiple claims—even when names are misspelled or data is incomplete.
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Identification of complex patterns of potential fraud likely missed by less sophisticated AI
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Prevention of financial losses at scale
The power of insurance-grade AI and real-time detection
To illustrate, US-based Dmitri Korin shares an example from one of our customers. In this case, Shift successfully uncovered a fraudulent hit-and-run claim involving passengers and a vehicle with prior SIU investigations. More specifically, Shift's AI was able to identify that that the passengers involved had two prior SIU investigations, and that the vehicle itself also had a previous investigation. This triggered an alert and subsequent investigation that likely would have been missed with less advanced AI models. In the investigation, the insurer discovered that the passenger and driver had switched places at the very last second, and they were able to deny the claim.
Without Shift’s AI, uncovering these cases would be nearly impossible. Purpose-built for the insurance industry, Shift’s models are trained specifically for complex reconstruction tasks. They apply thousands of intelligent rules and advanced data-linking techniques to accurately connect people and vehicles across multiple claims—even when names are misspelled or data is incomplete. In this case, deep data reconstruction enabled Shift to piece together prior investigations involving both the passengers and the vehicle. By intelligently matching these entities, the models revealed connections that would have been extraordinarily difficult to detect otherwise.
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
