Victoria Scam & Telecom Incident Report – May 2025

Overview of reported telecommunications incidents across Victoria in May 2025. This report captures community-sourced reporting activity between 1–31 May 2025, analysing scam classification patterns, regional distribution, and emerging safety signals.

Executive Summary

This report analyses community-submitted telecommunications safety data across Victoria between 1–31 May 2025. All classifications, trend observations, and regional patterns are derived from first-hand community intelligence aggregated through the Reverseau platform.

Victoria recorded 1,814 community reports across 1,233 unique phone numbers during the reporting period. Compared to April 2025, reporting volume showed a slight decrease of 20%, while 1,233 numbers remained under active community monitoring throughout the month.

Scam remains the most frequently assigned community classification at 61% of categorised reports, with a scam classification ratio of 61% 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 Melbourne, followed by Geelong and Dandenong. Melbourne recorded more than double the reporting volume of the next most active locality (Geelong), indicating concentrated campaign activity or higher community engagement within this area.

May continues the pre-EOFY scam surge, with tax refund and superannuation scam campaigns frequently observed. Business-targeting activity also tends to increase.

The sustained dominance of scam classifications at 61% suggests structural targeting behaviour rather than isolated campaign spikes, warranting continued community vigilance. Residents are encouraged to report suspicious telecommunications activity and consult the VIC data dashboard for real-time classification and trend data.

Why This Matters

Sustained scam classification dominance at 61% across consecutive reporting periods suggests structural targeting patterns rather than isolated campaign surges. When a single classification category maintains this level of prevalence, it indicates persistent, organised activity that is unlikely to self-correct without sustained community awareness. Continued monitoring across Victoria’s metropolitan and regional areas remains critical to early detection of coordinated telecommunications fraud and to building the community intelligence layer that enables faster classification convergence on emerging threats.

Community Reports
1,814
vs April 2025 -20%
Unique Numbers Reported
1,233
Scam Classification Ratio
61%
Active Numbers Monitored
1,233

Scam Category Breakdown

Community classification distribution across VIC for the period 1–31 May 2025. Classifications are assigned by reporting users based on their direct experience with each number.

Scam61%
Spam20%
Suspicious14%
Legit2%
Uncertain2%

Scam accounted for 61% of categorised reports during May 2025. In April 2025, Scam held the top position with 67% 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 Victoria

Localities with the highest concentration of community reports during 1–31 May 2025. Each locality links to its dedicated intelligence page with full classification breakdowns and number listings.

Melbourne recorded more than double the reporting volume of the next most active locality (Geelong), 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 VIC data dashboard.

Month-to-Month Comparison

Compared to April 2025, Victoria experienced a slight decrease of 20% in community reporting volume. Overall activity has decreased, with substantial monitoring coverage across the state.

Seasonal Context

May continues the pre-EOFY scam surge, with tax refund and superannuation scam campaigns frequently observed. Business-targeting activity also tends to increase. The observed decrease of 20% may reflect seasonal reporting variation, reduced campaign activity, or shifts in community engagement patterns during this period.

Classification Movement

Scam classifications accounted for 61% of categorised reports in May, with scam-specific reports representing 61% 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

Melbourne 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 VIC accumulated multiple community reports within a compressed time window during 1–31 May 2025. 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 8 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 VIC. 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 May 2025. 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 Victoria.
  • 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 1–31 May 2025. All data is aggregated and anonymised before publication.

  • Source: First-hand community reports submitted via Reverseau.
  • Scope: Numbers with a registered allocation within Victoria (VIC).
  • Period: 1–31 May 2025 (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 VIC data dashboard.