Who is it for?
Built for insurance intelligence, claims, and special investigations teams assessing suspect claims on contested medical evidence. Most useful for teams deciding whether a claim justifies the cost of physical surveillance.
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Claims Integrity Assessment Using Open-Source Intelligence
See how an analyst moves from a name, an email address, and a phone number to a confirmed digital footprint, publicly posted activity, and an associated network.
Suspect claims are among the hardest cases an insurance team has to assess. The claimed incapacity is invisible, the medical evidence is contested, and the traditional answer is physical surveillance: expensive, slow, and dependent on being in the right place at the right time.
This case study walks through a Tradecraft demonstration built entirely on publicly available data. No surveillance was deployed and no claim was under assessment.
What is claims integrity assessment using OSINT?
Before an insurer commits a surveillance team, publicly available data can indicate whether the picture already on the public record is consistent with what a claim asserts. Fivecast ONYX searches more than 300 open sources, including news, search engines, company databases, sanctions lists, data leaks, and the dark web, from whatever selectors the analyst already holds.
How does an analyst build a confirmed digital footprint?
Discovery returns a network chart showing how selectors connect: which email addresses link to which phone numbers, which social accounts sit behind them, and where the subject appears in media mentions. Chaining selectors together produces confidence that the accounts under review belong to the same person, which in an assessment context is the difference between an indicator and a coincidence.
What does targeted collection surface?
Customizable risk detectors surface text, keywords, objects, and logos from large volumes of digital noise. In the demonstration, collection returned posts documenting a gym routine where plate loadings were printed on the equipment and legible in frame, so Optical Character Recognition (OCR) read them automatically and the activity could be quantified rather than estimated.
What does network analysis add?
Network analysis can indicate possible collusion between businesses through mutual connections that corporate registries do not show, surface links to criminal organizations, and connect a new claimant to persons already known to an investigations team. Data that has been collected stays collected, so a subject reviewed today becomes a reference point tomorrow.
How does this stay proportionate?
Nothing surfaced through open-source intelligence is a conclusion on its own, and none of it replaces the assessor’s judgment. What changes is the sequence: open-source intelligence establishes what is already knowable before costly capabilities are committed.
What you’ll learn
- How discovery across 300+ sources builds a verified digital footprint from minimal selectors
- Why chaining selectors is what separates an indicator from a coincidence
- How customizable risk detectors and OCR quantify activity rather than estimate it
- How network analysis surfaces associations an insurer’s own systems would never hold
- Where open-source intelligence sits relative to surveillance, examination, and interview
