Australian Scam Trends

This chart plots scam checks per day. Days with fewer than 10 scans are never shown, so the line appears once daily activity clears that floor on at least 7 days in the window.

This chart breaks scans down by how the suspicious content arrived: SMS, email, website, phone call or QR code. It appears once the 30-day total clears the publication floor.

Every scan gets one of four verdicts: Safe, Caution, Suspicious or Scam. Once the 30-day total clears the publication floor, this bar shows the mix.

At each month's close we freeze that month's numbers into a permanent, citable report: the Scam Scanner Index. Frozen numbers never change after publication.

These statistics come from the Australian Scam Report, Scam Scanner's free public research programme. When a Scam Scanner user checks something, the app records one anonymous, content-free entry: the risk verdict (Safe, Caution, Suspicious or Scam), the type of thing checked (for example a text message, email or website), and any well-known brand the content imitated, matched against our built-in brand list. Collection began on 3 July 2026.

• No content, no identity. We never record what was scanned: not the message, image, URL, phone number or any text. Entries carry no name, account, device ID or IP address, and time is recorded only to the hour. See our privacy policy (section 2.5). Users can turn this off in the app's Settings.
• Minimum published count of 10. No figure below 10 is ever published, anywhere on this site. Small counts are either folded into an “Other” category or withheld entirely, so no published number can describe a small group.
• Rounded down. Every published count is rounded down to the nearest 10, which is why figures read as “120+”.
• Australian Eastern Time. Days and months are counted in AEST/AEDT (Australia/Sydney). This does not change.
• Scam Scanner users, not all Australians. These are counts of scans run by people who use our app. They are not a measure of Australian scam rates, and we never extrapolate from them. For population-level figures, see Scamwatch's scam statistics.
• Known residual. If the same image is re-scanned within about 24 hours it can occasionally be counted twice. This biases counts slightly upward, uniformly, and is disclosed rather than corrected.

Scam numbers are easy to misread, and reporting on them often does. Three principles guide everything published on this page:

• A sample is not a population. Our data describes what Scam Scanner users checked, which over-represents people who are already suspicious of a message. A brand topping our leaderboard means our users saw many scams in its name, not that the brand is dangerous, and not that it tops all-of-Australia figures.
• Small counts mislead. With a handful of scans, a single household can swing a percentage wildly, and rare categories can accidentally describe identifiable people. That is why nothing below 10 is ever published and why sections stay hidden until totals are large enough to be meaningful.
• No extrapolation. We publish what we counted, rounded down. We never scale our numbers up to national estimates, and any citation of this page should keep the “Scam Scanner users” framing.

What will be published, and when: headline totals once a 30-day window has 100+ scans; the daily chart once at least 7 days each clear 10 scans; the brand leaderboard once 3 or more brands clear 10 flagged scans in a month; and a monthly Scam Scanner Index for each month with 100+ scans. Everything activates automatically as those thresholds clear.

This data is published under CC BY 4.0: reuse it freely, with attribution to Scam Scanner. Suggested citation:

Scam Scanner, Australian Scam Trends (Australian Scam Report), retrieved 2026-07-03, tryscamscanner.com/trends

In prose: “according to Scam Scanner's Australian Scam Trends index”.

Worried about one message right now? Check it against our guides: brand impersonation checks covering 97 Australian brands, common scam types, or run the content through the free web checker.