Why the Scams Prevention Framework Requires a New Category: Actionable Scam Intelligence

Abstract

The Scams Prevention Framework (SPF) signals a shift from transaction-based fraud detection toward earlier detection and disruption of scam operations.

Achieving this shift requires a new operational capability: Actionable Scam Intelligence, which connects campaign signals, scam infrastructure, and monetisation pathways across the scam ecosystem.

The Shift Introduced by the Scams Prevention Framework

The introduction of Australia's Scams Prevention Framework (SPF) represents a fundamental shift in how organisations are expected to prevent scams. For a broader explanation of the framework and its objectives, see our overview of What Is Australia's Scams Prevention Framework.

Historically, fraud prevention systems have focused primarily on identifying suspicious financial transactions once they occur. While this capability remains essential, it addresses scams only at the final stage of the attack.

In reality, most scams begin much earlier in the digital ecosystem.

They often start with phishing infrastructure, impersonation campaigns, fraudulent advertisements, or coordinated social engineering operations targeting victims across multiple platforms.

The Scams Prevention Framework recognises that effective prevention requires earlier detection, improved cross-sector collaboration, and stronger disruption capabilities.

Achieving these goals requires a new operational capability that many organisations do not yet possess: Actionable Scam Intelligence.

The Limitations of Traditional Fraud Detection

Financial institutions and digital platforms have invested heavily in fraud detection technologies designed to identify unusual financial behaviour.

These systems typically analyse signals such as:

• abnormal transaction patterns
• unusual login behaviour
• device or location anomalies
• historical fraud indicators

While these systems remain essential, they are primarily designed to detect fraud after the victim has already been manipulated by a scammer.

In many scam scenarios, the criminal operation unfolds across several stages before any financial transaction occurs.

For example:

1. A victim receives a phishing SMS message impersonating a bank.
2. The message directs the victim to a fake website.
3. The victim submits login credentials or personal information.
4. The scammer uses this information to manipulate the victim or access financial systems.
5. Only then does the financial transaction occur.

By the time the bank detects the suspicious payment, the scam operation may already have interacted with multiple victims through infrastructure operating outside the institution's systems.

This gap between where scams originate and where financial loss occurs is one of the central challenges addressed by the Scams Prevention Framework.

Understanding the Scam Lifecycle

To understand why intelligence is important, it is helpful to view scams not as isolated incidents but as coordinated operations.

Most large-scale scams involve multiple components working together.

These may include:

• impersonation domains mimicking legitimate brands
• phishing websites used to capture credentials
• messaging campaigns targeting thousands of victims
• social engineering scripts used by scam operators
• networks of mule accounts used to move stolen funds

Together, these elements form what can be described as scam infrastructure.

From a technical perspective, this infrastructure may span multiple systems, including:

• domain registrars
• hosting providers
• messaging platforms
• social media platforms
• payment networks

No single organisation has complete visibility of this entire system.

Without intelligence capabilities that connect signals across these components, organisations may struggle to identify the broader scam campaign behind individual incidents.

The Intelligence Gap in Scam Prevention

One of the challenges highlighted by the Scams Prevention Framework is the gap between the signals organisations currently monitor and the signals required to detect scams earlier.

Most financial institutions primarily observe signals within their own environment.

Examples include:

• suspicious transactions
• unusual login behaviour
• customer complaints

However, important indicators of scam activity often exist outside these systems.

Examples include:

• newly registered phishing domains targeting a brand
• clusters of impersonation websites sharing similar infrastructure
• scam campaigns targeting customers through messaging platforms
• patterns of victim reports linked to a single operation

These signals may exist across different industries, platforms, or jurisdictions. Without the ability to collect, validate, and correlate these signals, organisations may struggle to identify coordinated scam campaigns.

This intelligence gap is where Actionable Scam Intelligence becomes essential.

Not All Scam Intelligence Starts With a Brand Signal

Many scam detection approaches begin by monitoring for brand impersonation. While brand abuse remains an important signal, it is not the only starting point for identifying scam operations.

In many scam types, the first detectable indicators may instead involve:

• suspicious receiving accounts
• repeated payment destinations across victims
• behavioural patterns inside messaging groups
• scam conversations occurring within digital platforms
• repeated scam patterns linked to a specific operator

In these scenarios, the most valuable signal may not be the brand being impersonated. Instead, the critical insight may come from the monetisation pathway used by scammers.

In some scam types, the most valuable signal is not the brand being misused, but the monetisation path that links multiple victims to the same operator or scam network.

Understanding these monetisation pathways can provide powerful insights into how scam operations function and where intervention may be possible.

Definition: Actionable Scam Intelligence (ASI)

Actionable Scam Intelligence (ASI) refers to analysed and validated insight about scam operations that enables organisations to detect, understand, and disrupt scams before financial harm occurs.

Unlike raw threat data or isolated incident reports, actionable intelligence focuses on producing insights that support real operational decisions. It connects signals across the scam ecosystem and reveals where intervention is possible.

Within modern scam prevention, Actionable Scam Intelligence typically involves three interconnected intelligence layers.

Campaign Intelligence

Campaign intelligence focuses on identifying coordinated scam operations targeting victims at scale.

This may include analysing:

• scam messaging campaigns
• impersonation themes
• victim targeting patterns
• recurring social engineering narratives

Campaign intelligence helps organisations recognise that multiple incidents may be part of the same scam operation.

Infrastructure Intelligence

Infrastructure intelligence focuses on identifying the technical systems used by scammers.

This may include:

• phishing domains
• hosting infrastructure
• malicious websites
• fake application distribution
• messaging infrastructure used to distribute scam content

By mapping this infrastructure, organisations can identify clusters of activity associated with a particular scam campaign.

Monetisation Intelligence

Monetisation intelligence focuses on understanding how scam operations ultimately convert deception into financial gain.

This may involve identifying:

• mule accounts receiving stolen funds
• payment destinations linked to multiple victims
• patterns of account usage associated with scam operations
• relationships between receiving accounts and infrastructure used in scams

Monetisation intelligence is often one of the most powerful ways to identify connections between seemingly unrelated incidents.

By analysing payment destinations and financial flows, organisations can uncover networks linking multiple victims to the same scam operators.

Actionable Means More Than Knowing a Scam Exists

Simply identifying suspicious content or isolated incidents does not necessarily enable organisations to stop scam operations.

For intelligence to be truly actionable, it must reveal where intervention is possible.

Effective scam intelligence should enable organisations to:

• identify scam infrastructure that can be disrupted
• locate payment endpoints used to receive stolen funds
• uncover mule accounts supporting scam monetisation
• detect relationships between victims and scam operators
• identify intervention points where scam operations can be disrupted

In practice, intelligence is only actionable if it reveals where intervention is possible — not just what content is suspicious.

From Data to Intelligence

A key distinction between data and intelligence lies in the analysis and validation process.

Many organisations already collect large volumes of data related to scams.

Examples include:

• customer scam reports
• suspicious domain observations
• phishing detection alerts
• incident investigation records

However, raw data alone does not necessarily produce useful insight.

Actionable intelligence requires several additional stages.

Collection

Signals must first be gathered from multiple sources, including user reports, infrastructure monitoring, open-source intelligence, and investigative activity.

Validation

Signals must then be verified to ensure they represent genuine scam activity rather than false positives.

Correlation

Multiple signals are analysed together to identify relationships and patterns. For example, several phishing domains may share common infrastructure or registration details.

Analysis

Analysts or automated systems interpret these relationships to identify scam campaigns, infrastructure clusters, or behavioural patterns.

Operationalisation

Finally, intelligence must be translated into actions. These may include disrupting scam infrastructure, blocking malicious accounts, or alerting relevant stakeholders.

Only when these steps are completed does data become actionable intelligence.

Why Intelligence Matters for SPF

The Scams Prevention Framework encourages organisations to detect and disrupt scams earlier in their lifecycle.

To achieve this goal, institutions must be able to identify scam activity before victims reach the point of financial loss.

Actionable scam intelligence provides visibility into scam operations at these earlier stages.

For example, intelligence about phishing infrastructure targeting a bank may allow the institution to:

• identify scams before customers interact with them
• coordinate disruption efforts with hosting providers
• warn customers about active scam campaigns

This proactive approach can significantly reduce the number of successful scam attempts.

Financial institutions in particular face new operational expectations under SPF. For a detailed discussion of how the framework affects banks, see What SPF Means for Banks and Financial Institutions.

The emphasis on earlier detection and disruption aligns with regulatory discussions surrounding the Scams Prevention Framework, which aim to strengthen cooperation between financial institutions, digital platforms, and telecommunications providers in addressing scam activity across sectors.

(Source: Australian Treasury – Scams Prevention Framework Consultation Paper)

The Australian Competition and Consumer Commission (ACCC) has also emphasised the importance of coordinated intelligence sharing through initiatives such as the National Anti-Scam Centre.

(Source: ACCC – National Anti-Scam Centre)

Intelligence as the Foundation of Scam Disruption

Another key element of the Scams Prevention Framework is the emphasis on disruption.

Disruption refers to actions taken to stop scam operations from continuing.

This may include removing phishing websites, blocking fraudulent accounts, or coordinating responses across multiple organisations.

However, disruption is difficult without intelligence.

To dismantle a scam operation, organisations must first understand:

• where the scam infrastructure is located
• how different components of the campaign are connected
• which organisations are affected
• how victims are being monetised

Intelligence provides the visibility required to identify these components and coordinate disruption efforts effectively.

Building an Intelligence-Led Scam Prevention Model

As organisations adapt to SPF expectations, many are beginning to adopt an intelligence-led model for scam prevention.

This model typically involves several interconnected capabilities:

• collecting signals from customers, infrastructure monitoring, and investigations
• analysing signals to identify patterns and relationships
• producing intelligence reports about scam campaigns
• supporting disruption of scam infrastructure
• sharing relevant intelligence with industry partners

By integrating these capabilities, organisations can move from reacting to individual incidents toward identifying and disrupting scam operations at scale.

The Emergence of a New Category

The increasing importance of intelligence in scam prevention is leading to the emergence of a new operational category within cybersecurity and fraud management.

Traditional fraud systems focus primarily on:

• transaction monitoring
• identity verification
• payment risk controls

In contrast, this new category focuses on understanding and disrupting scam operations themselves.

Actionable Scam Intelligence sits at the centre of this capability.

It connects signals from multiple sources, reveals relationships between scam incidents, and provides the operational insight required to disrupt scams before financial harm occurs.

As regulatory expectations evolve and scam operations continue to grow in sophistication, intelligence-led approaches are likely to become an increasingly important component of modern scam prevention strategies.

Conclusion

The Scams Prevention Framework represents a significant shift in how organisations approach scam prevention.

Preventing scams is no longer solely about detecting suspicious transactions. It requires earlier visibility into scam operations, improved collaboration across sectors, and the ability to disrupt criminal infrastructure before victims suffer financial harm.

Actionable Scam Intelligence plays a critical role in enabling this shift.

By transforming raw signals into operational insight, intelligence allows organisations to identify scam campaigns, understand their infrastructure, and intervene at the points where scam operations generate financial harm.

As scams continue to evolve and regulatory expectations increase, intelligence-driven approaches will become an essential component of effective scam prevention.

Frequently Asked Questions