How Fakeshop AI calculates its trust score
No black-box guesswork: we disclose which signals we check, how they are weighted and where our data comes from. The score is guidance – an automated assessment, not legal advice.
How the trust score is built
Every check starts at 100 points. Each examined signal adds or subtracts weighted points – serious red flags (e.g. a missing legal notice, prepayment only, a match on a warning list) lower the score significantly, positive signals stabilise it. The result is a single, traceable number from 0 to 100, plus a reason for every signal.
A second AI check can uncover contradictions in the first assessment. Errors and missing data remain possible, so we show the checked signals and limits of the assessment.
Clear fraud signals – we advise against purchasing.
Unclear or mixed signals – caution and further checks advised.
Fewer warning signs in the available data – no safety guarantee.
Why we check automatically
A hand-curated list is a snapshot. It says how a shop looked on the day someone looked at it – and in this field that changes fast: domains get resold, legal notices swapped, entire shops continued under a new name. An entry without a date is therefore hard to place.
Each shop report shows when the last check took place. An older finding may not reflect the shop today; check current details and sources before buying.
The automated check evaluates available signals and explains its reasons. Sources and missing data should be visible. If an assessment appears wrong, you can report it through our correction process and we will review it.
The signals we check
More than 30 signals feed into every assessment. The most important groups:
Completeness under German law: company, address, trade register, VAT ID. Missing mandatory details lower the score significantly.
Very young domains (days to a few weeks) are a strong warning sign for short-lived fake shops.
Prepayment only is one of the strongest single signals. Buyer-protection options are stabilising.
Bundling several independent sources to tell fake review waves from genuine feedback.
We check whether a seal image actually links to a valid, official certificate – not just whether it is shown.
Unrealistically high permanent discounts on branded goods are a classic lure and warning sign.
Encryption is mandatory but no proof of trust on its own; suspicious hosting patterns feed in.
Cross-check against external sources. A “problematic shop” entry is not the same as a confirmed fraud warning.
Where our data comes from
We evaluate publicly available signals only: the legal notice and the website itself, public trade-register data, WHOIS/domain information, SSL certificates, publicly visible reviews (incl. Trustpilot, Trusted Shops) and public fake-shop warning lists from consumer-protection bodies. We buy no opaque data pools and invent no metrics.
Free, ad-free, independent
For consumers, Fakeshop AI is free and ad-free. We earn nothing from rating a shop well or badly – operations are funded by the paid brand-protection monitoring for businesses and primarily cover server costs. There are no paid "buy-yourself-out" options for rated shops.
Found an error? You can object
Automated assessments can be wrong. Affected shop operators can object to a rating at any time – we review every notice.
To the correction page