Tasmania Scam & Telecom Incident Report – April 2024

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

Executive Summary

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

Tasmania recorded 57 community reports across 37 unique phone numbers during the reporting period. Compared to March 2024, reporting volume showed a significant increase of 68%, while 37 numbers remained under active community monitoring throughout the month.

Suspicious remains the most frequently assigned community classification at 37% of categorised reports, with a scam classification ratio of 26% across all submissions. A classification shift was observed: Suspicious displaced Scam as the dominant category, which may indicate a transition in active campaign strategies or a change in community reporting behaviour.

Geographically, reporting activity was concentrated in Hobart, followed by Geeveston and Apollo Bay. Reporting activity was moderately concentrated in Hobart, though Geeveston also contributed notable volume, suggesting distributed targeting across multiple areas.

April coincides with peak tax scam season as the end of the Australian financial year approaches. ATO impersonation campaigns historically surge during this month.

Scam classifications account for 26% 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 TAS data dashboard for real-time classification and trend data.

Why This Matters

While scam classifications represent a smaller share of overall reporting at 26%, the diversity of classification categories observed across Tasmania underscores the importance of community-driven monitoring. Telecommunications safety extends beyond scam detection — nuisance, telemarketing, and unknown classifications each contribute to a more complete picture of how phone numbers interact with the community. Continued reporting across all categories strengthens the analytical foundation that powers early detection and trend visibility.

Community Reports
57
vs March 2024 +68%
Unique Numbers Reported
37
Scam Classification Ratio
26%
Active Numbers Monitored
37

Scam Category Breakdown

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

Suspicious37%
Scam26%
Spam18%
Uncertain16%
Legit4%

Suspicious accounted for 37% of categorised reports during April 2024. In March 2024, Scam held the top position with 41% of classifications. A classification shift was observed: Suspicious displaced Scam as the dominant category, which may indicate a transition in active campaign strategies or a change in community reporting behaviour.

Most Affected Areas in Tasmania

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

Reporting activity was moderately concentrated in Hobart, though Geeveston also contributed notable volume, suggesting distributed targeting across multiple areas. For detailed locality-level analysis, visit the individual area pages linked above or explore the TAS data dashboard.

Month-to-Month Comparison

Compared to March 2024, Tasmania experienced a significant increase of 68% in community reporting volume. Overall activity has increased, with moderate monitoring coverage across the state.

Seasonal Context

April coincides with peak tax scam season as the end of the Australian financial year approaches. ATO impersonation campaigns historically surge during this month. The observed increase of 68% aligns with typical post-seasonal campaign escalation, where scam operators increase targeting activity in response to changing consumer behaviour patterns.

Classification Movement

Suspicious classifications accounted for 37% of categorised reports in April, with scam-specific reports representing 26% 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

Despite the overall increase in reporting volume, Hobart remained the primary reporting hub. Elevated reporting in Hobart may reflect both population density effects and localised campaign activity rather than a uniform state-wide increase.

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

5 numbers within TAS accumulated multiple community reports within a compressed time window during 1–30 April 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.

Flagged numbers averaged 4 reports each, consistent with early-stage campaign detection where community awareness is still building.

Several flagged numbers exhibited cross-locality reporting dispersion, with community submissions originating from multiple areas within TAS. 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 April 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 03 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 Tasmania.
  • 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 April 2024. All data is aggregated and anonymised before publication.

  • Source: First-hand community reports submitted via Reverseau.
  • Scope: Numbers with a registered allocation within Tasmania (TAS).
  • Period: 1–30 April 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 TAS data dashboard.