Queensland Scam & Telecom Incident Report – November 2024

Overview of reported telecommunications incidents across Queensland in November 2024. This report captures community-sourced reporting activity between 1–30 November 2024, analysing scam classification patterns, regional distribution, and emerging safety signals.

Executive Summary

This report analyses community-submitted telecommunications safety data across Queensland between 1–30 November 2024. All classifications, trend observations, and regional patterns are derived from first-hand community intelligence aggregated through the Reverseau platform.

Queensland recorded 639 community reports across 418 unique phone numbers during the reporting period. Compared to October 2024, reporting volume showed a slight decrease of 17%, while 418 numbers remained under active community monitoring throughout the month.

Scam remains the most frequently assigned community classification at 34% of categorised reports, with a scam classification ratio of 34% across all submissions. Scam maintained its position as the dominant classification in both periods, suggesting sustained targeting patterns rather than campaign rotation.

Geographically, reporting activity was concentrated in Brisbane, followed by Cairns and Townsville. Brisbane recorded more than double the reporting volume of the next most active locality (Cairns), indicating concentrated campaign activity or higher community engagement within this area.

November is characterised by Black Friday and Cyber Monday shopping scams. Parcel delivery impersonation and payment fraud campaigns reach elevated levels.

Scam classifications account for 34% of reports, suggesting a mixed telecommunications activity landscape where non-scam reporting categories play a significant role in the overall safety picture. Residents are encouraged to report suspicious telecommunications activity and consult the QLD data dashboard for real-time classification and trend data.

Why This Matters

The proportion of scam-classified reports at 34% indicates active but evolving targeting patterns across Queensland. Understanding these patterns at a community level enables faster identification of emerging campaign types and reduces the window between first contact and community-wide awareness. Sustained reporting activity across multiple localities strengthens the collective intelligence foundation, allowing classification convergence to accelerate as more residents contribute first-hand safety data to the QLD reporting ecosystem.

Community Reports
639
vs October 2024 -17%
Unique Numbers Reported
418
Scam Classification Ratio
34%
Active Numbers Monitored
418

Scam Category Breakdown

Community classification distribution across QLD for the period 1–30 November 2024. Classifications are assigned by reporting users based on their direct experience with each number.

Scam34%
Suspicious22%
Spam21%
Legit13%
Uncertain10%

Scam accounted for 34% of categorised reports during November 2024. In October 2024, Scam held the top position with 36% of classifications. Scam maintained its position as the dominant classification in both periods, suggesting sustained targeting patterns rather than campaign rotation.

Most Affected Areas in Queensland

Localities with the highest concentration of community reports during 1–30 November 2024. Each locality links to its dedicated intelligence page with full classification breakdowns and number listings.

Brisbane recorded more than double the reporting volume of the next most active locality (Cairns), indicating concentrated campaign activity or higher community engagement within this area. For detailed locality-level analysis, visit the individual area pages linked above or explore the QLD data dashboard.

Month-to-Month Comparison

Compared to October 2024, Queensland experienced a slight decrease of 17% in community reporting volume. Overall activity has decreased, with substantial monitoring coverage across the state.

Seasonal Context

November is characterised by Black Friday and Cyber Monday shopping scams. Parcel delivery impersonation and payment fraud campaigns reach elevated levels. The observed decrease of 17% may reflect seasonal reporting variation, reduced campaign activity, or shifts in community engagement patterns during this period.

Classification Movement

Scam classifications accounted for 34% of categorised reports in November, with scam-specific reports representing 34% of all submissions. These shifts in community classification patterns may reflect evolving campaign tactics, changes in the types of numbers being reported, or natural variation in reporting behaviour between periods. Monitoring classification movement over consecutive months provides a more reliable indicator of genuine trend shifts than any single-month comparison.

Regional Variation

Brisbane maintained its position as the most active reporting locality even as overall volumes declined. This persistence suggests that reporting behaviour in metropolitan areas is more resilient to volume fluctuations than regional submissions.

Service Type Distribution

Local Service100%

Local Service numbers account for 100% of reported activity, reflecting the broader national pattern where mobile-originated calls dominate community safety reports. Residents should exercise particular caution with unsolicited calls from unfamiliar local service numbers.

Emerging Trends & Observations

Several numbers exhibited accelerated reporting velocity within compressed time windows, followed by classification convergence toward scam designation.

Rapid Accumulation Signals

10 numbers within QLD accumulated multiple community reports within a compressed time window during 1–30 November 2024. This velocity pattern is consistent with active call campaigns or coordinated targeting activity. Numbers exhibiting rapid report accumulation frequently transition from initial “Unknown” or “Suspicious” classifications to confirmed “Scam” designation within days.

Numbers flagged for rapid accumulation averaged 9 reports each during the period, indicating sustained community engagement with these numbers rather than isolated encounters.

Several flagged numbers exhibited cross-locality reporting dispersion, with community submissions originating from multiple areas within QLD. This pattern suggests broadcast-style outbound activity rather than localised outreach, consistent with automated dialling campaigns that target numbers across geographic boundaries.

Divergent Classification Signals

Several numbers display mixed community classifications — receiving both scam and non-scam reports during November 2024. This divergence may indicate numbers transitioning between legitimate and illegitimate use, caller ID spoofing of legitimate business numbers, or community uncertainty about the nature of calls received. Numbers with divergent classifications warrant continued monitoring as community consensus develops.

Community Safety Guidance

  • Do not return missed calls from unknown 07 numbers without verification.
  • Verify any government agency claims through official websites or published contact numbers — the ATO, Centrelink, and Medicare will never threaten immediate action via phone.
  • Avoid clicking payment or delivery links received via SMS from unrecognised senders.
  • Report suspicious telecommunications activity to help build community safety intelligence for Queensland.
  • Check numbers on Reverseau before returning calls from unknown sources.

Data Methodology

This report is compiled from community-submitted telecommunications safety reports for the period 1–30 November 2024. All data is aggregated and anonymised before publication.

  • Source: First-hand community reports submitted via Reverseau.
  • Scope: Numbers with a registered allocation within Queensland (QLD).
  • Period: 1–30 November 2024 (calendar month).
  • Classifications: Assigned by reporting users based on their direct experience.
  • Limitations: Data reflects community perception, not verified telecommunications records. Reporting volumes are influenced by platform adoption and user engagement patterns.

For detailed methodology, see our methodology page. For the full analytical dataset, visit the QLD data dashboard.