Shift Technology for Insurance Claims and Fraud Operations

In-Depth Review

Shift Technology was founded in Paris in 2014 specifically to apply machine learning to insurance fraud detection, at a time when most carriers were still relying on rules-based scoring and manual SIU referrals. The company has since expanded from fraud detection into claims automation and underwriting AI, but fraud detection remains the core product and the area where its track record is most established.

What Shift Technology Does for Insurance Operations

The central product is FORCE, the fraud detection engine. It scores incoming claims using a combination of supervised machine learning, network graph analysis, and configurable rule sets. Unlike generic fraud tools, the models are trained on insurance-specific scenarios — staged accidents, inflated repair estimates, medical billing fraud, workers compensation malingering — across multiple lines of business.

The network analysis component is where FORCE differentiates itself most clearly. Rather than scoring each claim in isolation, it builds relationship graphs connecting claimants, attorneys, medical providers, body shops, and other parties across your entire claims portfolio. This makes organized fraud rings visible that would never trigger a single-claim score: a body shop appearing repeatedly across unrelated claims from the same geographic area, or an attorney linked to an unusual cluster of soft-tissue injury claims.

SAAM, the claims automation module, handles the other direction: identifying claims that are straightforward enough to process without adjuster review. For routine auto physical damage or simple property claims where documentation is complete and no fraud flags are present, SAAM enables straight-through processing that settles the claim and frees adjusters to focus on complex cases.

Key Features

Decision explainability is worth calling out as a practical feature rather than a marketing point. Each fraud alert includes a plain-language explanation listing the specific signals that contributed to the score. This matters for two reasons: adjusters can evaluate whether to act on an alert without treating it as a black box, and the explanation serves as documentation if a denial is challenged by a claimant or reviewed by a state insurance department.

Underwriting AI extends the platform beyond claims. It applies historical claims and behavioral data to score policies at the point of new business or renewal, flagging accounts with elevated risk before a claim is filed. This is a less mature product than the fraud detection modules, but it represents a logical extension for carriers already running Shift on the claims side.

Pricing

Shift Technology does not publish pricing. All contracts go through enterprise sales. Based on published case studies and industry discussions, deployments typically involve multi-year agreements with pricing tied to claims volume. There is no self-serve tier or small-carrier offering — the platform is built for mid-to-large carriers with substantial annual claim counts.

If you are evaluating Shift, expect to provide three to five years of historical claims data as part of the scoping process. The sales team will use that data to model expected fraud detection lift and generate an ROI estimate. Treat that estimate as directional, not binding.

Who This Is Best For

Shift Technology fits carriers running 50,000 or more claims annually in auto, property, or workers compensation, with the data infrastructure to support API integration with their core claims system. It is particularly well-suited to organizations that have already built out an SIU function and want to improve referral quality — shifting SIU caseload from low-confidence manual flags to high-confidence AI-scored alerts.

It is not a good fit for small regional mutuals, carriers on legacy platforms without API capability, or organizations without dedicated claims analytics staff to manage model configuration and threshold tuning.

One Thing to Test Before Committing

Before signing a contract, request a proof-of-concept run against your own claims data — specifically on a line where you have strong ground truth about confirmed fraud cases. Measure the model’s precision and recall on that historical dataset. The company will have aggregate statistics from other deployments, but performance on your specific book, with your specific provider networks and geographic mix, is what matters. If they are unwilling to do this before contract signature, that is informative.