Scams

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Scam Awareness Week is the headline, but the real story is a structural shift: fraud prevention is moving from compliance footnote to board-level performance driver. ANZ’s 15% reduction in scam losses and $100 million in recoveries signals what disciplined execution can achieve. The industry’s Scam‑Safe Accord shows welcome coordination, yet the technology and regulatory stakes are rising as generative AI turbocharges both attacks and defences. For banks, telcos and platforms, the winners will be those who convert trust and verification into measurable growth and lower cost-to-serve.

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Key implication: Scam prevention has become a core operating capability that directly shapes margin, growth and regulatory exposure. Awareness campaigns help, but the outperformers are industrialising controls, sharing intelligence across sectors and measuring fraud like a product KPI.

Business impact: Fraud is now a P&L line, not a poster on the wall

The Australian financial sector contributes roughly $140 billion to GDP and employs around 450,000 people; commercial banking revenue is estimated at $259.2 billion in 2025. Within this engine room of the economy, scams have evolved into a material earnings headwind. Industry studies suggest every $1 lost to fraud can cost three to four dollars once investigation, chargebacks, customer support and reputational drag are counted. That multiplier makes prevention a direct lever on cost-to-serve and customer lifetime value.

The prize is real. ANZ’s reported 15% drop in customer scam losses between October 2024 and June 2025 and recovery of over $100 million shows what coordinated investment can deliver. The ROI logic is straightforward: reduce net losses, cut back-office rework, lower call-centre volumes and protect deposit stickiness. This is why scam metrics now appear in board packs alongside credit losses and NPS.

Market context and competitive advantage: Trust is a growth vector

Heightened scam activity is forcing a reset of competitive dynamics. Trust—once an intangible—has hard commercial effects: higher digital adoption, lower abandonment, fewer disputes and improved cross-sell. Firms that make their controls visible (without adding clumsy friction) can price better, defend interchange, and reduce marketing spend per acquired customer.

Case in point: banks that invest in proactive “positive friction” (contextual warnings, stepped-up verification, delayed high-risk payments) consistently report lower loss rates and improved retention after incidents. BankWest’s internal training and rewards program—designed to elevate front-line scam detection—drove a meaningful lift in reported and intercepted scams, underscoring that culture and capability matter as much as algorithms.

Technical deep dive: Layered controls beat silver bullets

Modern scam defence operates on three planes:

• Signals and identity: Device fingerprinting, behavioural biometrics and session analytics build a live risk score. Abnormal typing cadence, new device–old account pairings, or geolocation mismatches can trigger step-up checks.
• Payment orchestration: Real‑time velocity checks and anomaly detection flag unusual payees or amounts, injecting just‑in‑time friction—warnings, temporary holds, or verified call‑backs. Tools like ANZ’s “Digital Padlock” illustrate customer‑visible controls that build confidence while deterring social engineering.
• Communications integrity: Verified sender frameworks for SMS and email, inbound call verification, and domain authentication reduce spoofing. Cross‑institution intelligence feeds help identify mule accounts and fast‑evolving scam typologies.

The effectiveness frontier is model governance: curbing false positives without letting attack rates creep back. Best‑in‑class teams tune models weekly, run adversarial testing, and deploy feedback loops from confirmed cases to shorten detection half‑life.

Coordination and regulation: From voluntary accords to shared liability

The joint Scam‑Safe Accord between the Australian Banking Association and the Customer Owned Banking Association is a welcome step toward common standards and data sharing. It acknowledges what the National Anti‑Scam Centre has pressed for since its launch: scams are a system problem spanning banks, telcos and digital platforms, not a single‑firm issue.

Global policy trends raise the stakes. The UK’s Payment Systems Regulator has moved to mandatory reimbursement for authorised push‑payment fraud, and Singapore’s proposed shared responsibility framework sets expectations for cost‑sharing across banks and telcos. The direction of travel is clear: more accountability, faster redress, tighter controls, and penalties for laggards. Australian institutions should plan for similar expectations—even before they become formal obligations.

Implementation reality: The hard yards between promise and performance

Execution is where awareness campaigns succeed or stall. Common pitfalls include:

• Blunt friction: Blanket holds frustrate good customers and drive leakage. The goal is precision friction informed by risk signals, not universal roadblocks.
• Channel silos: Scams traverse SMS, email, social and calls, while defences often sit in product silos. Unifying telemetry in a single decisioning layer is essential.
• Third‑party risk: Even strong bank controls can be undone by weak links in telcos or platforms. Contracts should mandate data‑sharing SLAs, takedown timelines and joint incident playbooks.
• Workforce readiness: Front‑line teams need playbooks, incentives and escalation paths. Firms that gamify detection and reward saves see materially better outcomes.

Measurement closes the loop. Leaders track loss rate per $1,000 of payments, detection time, recovery rate, customer‑contact avoidance, false positive ratio and post‑incident retention. If finance and risk can’t agree on these metrics, investments will wander.

Data‑driven trends: AI arms race and the trillion‑dollar problem

Global estimates from anti‑scam organisations peg annual scam losses at close to a trillion dollars, and the attack surface is expanding. Generative AI enables convincing deepfake voices and videos, lowers the cost of targeted phishing, and accelerates mule recruitment. The same technology, however, powers real‑time anomaly detection, synthetic identity screening and content authenticity checks. The firms that win will industrialise AI on both sides of the ledger—detection and customer education—while building strong model risk management and transparency.

Strategy playbook: Turning defence into durable advantage

Practical steps for executives:

• Elevate governance: Make scam risk a standing board agenda item; align accountability across risk, technology and product under a single P&L‑aware owner.
• Adopt a layered architecture: Combine behavioural biometrics, device intelligence, payment analytics and verified communications. Avoid vendor lock‑in; design for rapid model updates.
• Build precision friction: Introduce dynamic warnings, cooling‑off periods for first‑time payees, and risk‑based limits. Test customer comprehension, not just click‑throughs.
• Formalise cross‑sector MOUs: With telcos and platforms, agree on data formats, response times and joint takedowns. Plug into industry threat‑intel exchanges.
• Train and reward the front line: Treat scam detection as a save metric, not just compliance. Replicate BankWest‑style programs that recognise interceptions.
• Prepare for reimbursement regimes: Model impact under shared‑liability scenarios; create rapid adjudication workflows and reserve policies.

The bottom line: awareness is necessary, but operational excellence is decisive. In an era where AI amplifies both attack and defence, the institutions that measure, iterate and collaborate fastest will convert scam resilience into superior economics and market share.

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• Yes

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Scam Awareness Week is the headline, but the real story is a structural shift: fraud prevention is moving from compliance footnote to board-level performance driver. ANZ’s 15% reduction in scam losses and $100 million in recoveries signals what disciplined execution can achieve. The industry’s Scam‑Safe Accord shows welcome coordination, yet the technology and regulatory stakes are rising as generative AI turbocharges both attacks and defences. For banks, telcos and platforms, the winners will be those who convert trust and verification into measurable growth and lower cost-to-serve.

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Public-service messaging won’t stop modern scammers. Australia’s banks are moving from posters to prevention, blending AI, network controls and customer friction to change outcomes. ANZ’s program shows what good looks like, reporting a 15% drop in customer scam losses and more than $100 million in prevented and recovered funds in nine months. With liability rising and regulators tightening expectations, the economics now favour bold, proactive controls.

" ["fulltext"]=> string(8092) "

Case Study: Context

Australia’s financial sector is a $140 billion engine of GDP and employs roughly 450,000 people—making scams not just a consumer issue but a systemic operating risk. The National Anti-Scam Centre has set out to “make Australia a harder target for scammers,” while ASIC has repeatedly taken down investment scam sites misusing celebrity images. Yet the attack surface keeps expanding: social engineering now rides on deepfakes, spoofed caller IDs and hijacked business email threads. Research from FICO and others signals a regulatory turn—globally, banks face growing responsibility to compensate victims of authorised push payment (APP) scams, mirroring the UK’s mandatory reimbursement framework. Awareness weeks help, but the strategic challenge is to prevent losses at source without breaking the customer experience.

Decision

ANZ’s move from education-first to prevention-led fraud strategy offers a blueprint. Between October 2024 and June 2025, the bank reported a 15% reduction in customer scam losses and prevented/recovered over $100 million. The decision thesis: treat scams as a controllable cost of doing business and invest in a multi-layer defence that integrates technology, human intervention and ecosystem cooperation. The bank prioritised controls that intercept scams early (pre-payment), slow them in-flight (cool-offs and interdiction), and recover funds post-event (rapid freezing and mule-hunting). Governance shifted accordingly—board-level risk appetite, clear loss-ownership across product lines, and funding tied to measurable prevention outcomes rather than campaign reach.

Implementation

ANZ combined customer-centric protections with deep analytics and network action. Five layers define the operating model:

1) Payment initiation controls. Features such as ANZ’s Digital Padlock give customers the ability to lock down higher-risk payment types unless explicitly authorised, creating opt-in friction at the moment of risk. Step-up authentication for first-time payees, dynamic warnings, and cooling-off periods for flagged transfers slow the typical scam playbook of hurry-and-pay.

2) Identity and payee verification. Banks increasingly deploy name-matching alerts (e.g., “NameCheck”-style warnings) to flag mismatches before funds move. Behavioural biometrics and device fingerprinting monitor anomalies: keystroke patterns, unusual session speeds, new devices, or remote-access tools active on a customer’s handset—common markers in remote support scams.

3) Real-time transaction analytics. Machine learning models score outbound and inbound payments using features such as value spikes, payee risk, network velocity and mule typologies. Models are refreshed with recent scam campaigns (e.g., impersonations of government agencies or telcos), and degrees of confidence drive treatment paths from soft nudges to hard blocks.

4) Human-in-the-loop escalation. Specialist scam teams blend scripted diagnostics (“Are you on a call right now?”) with evidence-based guidance. Branch or video callbacks can validate high-value payments for vulnerable cohorts (e.g., first-time investors) without blanket frictions. A midwestern US community bank found that training and rewarding frontline staff for spotting social engineering lifted internal scam reporting materially and prevented high-value losses—proof that people remain a critical sensor network alongside AI.

5) Ecosystem coordination. Losses often exit the originating bank within minutes. ANZ’s results reflect faster cross-bank freezing of funds, tighter mule account offboarding, and cooperation with telecoms on SMS sender ID protections. ASIC’s takedowns and platform ad-quality controls shrink the top of the funnel, while the National Anti-Scam Centre improves signal sharing. The strategy recognises that no single bank can win a networked crime alone.

Results (with numbers)

ANZ’s reported outcomes are notable in a hostile environment: a 15% reduction in customer scam losses over nine months and more than $100 million in prevented and recovered funds. While every portfolio mixes differently, these numbers demonstrate that pre-emptive controls can shift loss curves even as scam volumes rise. If a bank invested, say, $20–30 million in the first year across analytics talent, model ops, platform features and ecosystem participation, the avoided losses at ANZ’s scale indicate a plausible multi‑x return on investment—before considering secondary benefits like lower complaint handling costs and reduced regulatory capital for operational risk.

Regulatory dynamics sharpen the business case. The UK’s Payment Systems Regulator is implementing mandatory APP reimbursement across sending and receiving banks, explicitly designed to “shift incentives upstream.” Consumer sentiment is aligned: surveys show growing expectations that banks reimburse scam losses. In short, preventing the payment is cheaper than reimbursing it. Meanwhile, global data breach studies (IBM reports average breach costs near US$5 million) underscore the combined cost of identity theft and account takeover that often precede scams.

Lessons

- Prevention beats awareness. Behavioural science is clear: in-the-moment frictions and defaults outperform education alone. Contextual warnings with clear choices reduce compliance bias.

- Design for adversaries, not averages. Scammers pivot fast. Treat models and rules as living products with weekly refreshes and red‑team exercises simulating current scam scripts.

- Close the recovery loop. Rapid freezing, network tracebacks and mule closures compound prevention gains. Measure minutes-to-freeze and closure rates alongside detection precision.

- Make customers co‑defenders. Opt-in locks, payment scheduling holds and default daily limits give users defensive posture by design—without needing to be fraud experts.

- Align incentives across the ecosystem. Shared liability and common standards (messaging registries, confirmation-of-payee, takedown SLAs) compress the scammers’ arbitrage space.

Technical deep dive: what’s working now

Best-in-class stacks fuse supervised ML (gradient-boosted trees for tabular transaction data) with anomaly detection (autoencoders for behaviour baselines) and graph analytics for mule rings. ISO 20022 payment enrichment offers more features per transaction—remittance purpose codes, structured names—to improve precision. On-device signals (SIM swap checks, integrity attestation) mitigate spoofed sessions. Feature stores and model governance reduce drift and bias, while real-time decision engines route cases to human review when latency budgets allow.

Future outlook

Three shifts will define the next two years: first, liability migration from consumers to institutions will continue, accelerating investment in pre‑payment controls and cross‑bank intelligence. Second, generative AI will industrialise impersonation—voice cloning, synthetic documents—raising the premium on out‑of‑band verification and secure consent frameworks. Third, network‑level defences—confirmation-of-payee at meaningful scale, scam intelligence exchanges, and coordinated mule eradication—will emerge as competitive differentiators. Early adopters will convert fraud budgets into margin protection and trust equity, while laggards face rising reimbursement costs, regulatory scrutiny and brand damage.

For Australian banks, the message is clear: move beyond awareness. Treat scams as an engineering and ecosystem problem with board‑level ownership. The ANZ case shows that decisive investment can measurably bend the loss curve, protect customers, and create a reputational moat in a market where trust is the ultimate currency.

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Public-service messaging won’t stop modern scammers. Australia’s banks are moving from posters to prevention, blending AI, network controls and customer friction to change outcomes. ANZ’s program shows what good looks like, reporting a 15% drop in customer scam losses and more than $100 million in prevented and recovered funds in nine months. With liability rising and regulators tightening expectations, the economics now favour bold, proactive controls.

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ANZ is warning customers to remain vigilant during the end of financial year period as scammers attempt to target Australians during tax time.

" ["fulltext"]=> string(2842) "

The bank has cautioned that scam tactics are becoming increasingly targeted, ranging from tax-related impersonation scams to fraudulent shopping websites offering massive EOFY discounts.

ANZ Head of Customer Protection Shaq Johnson said while the bank has measures in place to protect customers, preventing more than $50 million in suspicious transactions linked to scams between October 2023 and September 2024, customers must remain vigilant.

"Tax time is already a confusing period for many Australians and in the businesses of lodging a return it's easy to let your guard down," he said.

"Cybercriminals will look to exploit this, so it's crucial Australians understand the warning signs to avoid falling victim to a scam."

Johnson said scammers may use targeted, sophisticated tactics during the end of financial year period to steal money or personal information.

Common warning signs include unexpected text messages or emails claiming to be from the Australian Taxation Office or banks, alerting recipients to unclaimed refunds or outstanding tax bills.

"We're urging Australians to stop, think, and consider," Johnson said.

"If you are ever unsure that a message is legitimate, or simply would like the peace of mind, call your bank or relevant government organisation."

During tax time in 2024, ANZ experienced a spike in attempted rebate and extortion scams, with rebate scam volumes surging by 50 per cent in July before dropping sharply in August.

Extortion scams followed a similar trend, rising significantly in June by 41 per cent and July by 64 per cent.

Rebate scams involve scammers convincing victims they are entitled to a rebate from a bank or government service, asking for initial payments to cover administration fees.

Threats and extortion scams involve scammers demanding payments to avoid punishment such as arrest or deportation.

ANZ is also warning Australians about online shopping scams during EOFY sales periods.

"Australians love to snap up a bargain during the EOFY sales, and we also know scammers will attempt to take advantage of this," Johnson said.

"Common red flags to look out for when shopping online this EOFY include inconsistent or unfamiliar contact information, significantly cheaper offers compared with other sellers, or any deal that seems too good to be true."

The bank advises customers that in genuine ANZ communications, the bank will never ask customers to share sensitive banking details, click links to log into accounts, grant remote access to devices, or transfer money to other accounts.

ANZ operates customer protection teams 24/7, with customers who believe they may have been scam victims advised to contact the bank immediately on 13 13 14.

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ANZ is warning customers to remain vigilant during the end of financial year period as scammers attempt to target Australians during tax time.

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ANZ has issued a warning to customers about a potential rise in scam activity following the Reserve Bank of Australia's decision to lower the cash rate target by 25 basis points.

" ["fulltext"]=> string(2584) "

The bank said cyber criminals could exploit the rate change by posing as financial institutions or advisers in an attempt to steal money.

ANZ has urged customers to watch out for common scammer tactics following major financial announcements, including fake loan or mortgage offers, phishing links, promises of high-return investment opportunities and fake financial advice.

Shaq Johnson, ANZ head of customer protection, said customers should remain cautious at a time when they might be considering refinancing loans or securing strong rates for savings accounts.

"We urge customers to be hyper-vigilant around major financial announcements like these," Johnson said.

"Cybercriminals are using technology in increasingly sophisticated ways and always looking for new and convincing ways in.

"A change in the RBA cash rate could give them the opportunity to exploit confusion or create a false sense of urgency around locking in strong rates and transferring large sums of money."

Johnson said customers should be wary if something sounds too good to be true, noting that scammers often advertise higher savings rates on fake websites impersonating banks.

"It's important to cross-check all of the contact you receive directly with your bank," he said.

"If you are ever unsure, or you feel like you are being pressured to share sensitive information or lock in a deal, call or visit your bank to verify the contact and offer is legitimate."

The bank outlined several warning signs for investment scams, including suspiciously high interest rates, unexpected contact via SMS or messaging apps, and limited online presence of investment companies.

Other red flags include difficulty withdrawing funds from investment platforms, urgent tones in communications, requests to download remote access software, and cryptocurrency investment requirements.

ANZ also warned about recovery scams, where fraudsters contact previous scam victims claiming they can help recover lost funds for a fee.

The bank has launched its Scam Safe initiative to assist the community in remaining alert to constantly changing scams and fraud.

The program encourages customers to follow ANZ's security principles: Always be wary, Never share personal information with anyone, and Zoom in on the details as they matter.

ANZ's customer protection teams and systems operate 24/7, with customers who believe they may have been scam victims encouraged to contact the bank immediately on 13 13 14.

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ANZ has issued a warning to customers about a potential rise in scam activity following the Reserve Bank of Australia's decision to lower the cash rate target by 25 basis points.

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The Financial Basics Foundation has raised concerns over the impact of increasingly sophisticated digital marketing tactics on young consumers during Financial Literacy Month, highlighting the need for stronger financial education to counter the influence of online retailers like Shein and Temu.

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The not-for-profit organisation is urging schools, parents and young people to build awareness of how retailers use gamification, influencer marketing and hyper-personalised online experiences to encourage excessive spending among young Australians.

Katrina Samios, CEO of Financial Basics Foundation, said that financial literacy must evolve to address the modern pressures young people face online.

“Financial literacy isn’t just about saving, investing and interest rates,” Ms Samios said.

“It’s about being able to recognise value, making informed financial decisions and resisting the draw of manipulative marketing campaigns.”

“Retailers use sophisticated digital tactics to create a sense of urgency and drive impulse spending, often targeting young consumers who may not fully understand the long-term financial impact of their purchases.”

Recent data from Roy Morgan showed that Temu and Shein have more than 2 million monthly users in Australia, generating over $8 billion in annual sales. Gen Z consumers make up 15 per cent of Temu users and 23 per cent of Shein’s customer base.

These shoppers are drawn to discounts, social media recommendations and highly tailored suggestions, according to research by NIQ. Most users are repeat customers, with 40 per cent making four or more purchases in 12 months.

The foundation warned that e-commerce platforms are increasingly deploying gamified features—such as prize wheels, countdown deals and progress bars—to drive purchases. These features are designed to limit decision-making time and increase emotional responses, making it harder for young consumers to stick to budgets.

Social media platforms also play a key role in shaping shopping behaviour, with influencer-driven ‘hauls’ and trend-focused content encouraging fast fashion consumption and over-purchasing.

“E-commerce platforms are becoming increasingly sophisticated in how they lure young consumers, often using tactics that encourage impulse spending, making it easy to lose track of financial limits,” Ms Samios said.

To combat these challenges, the foundation is promoting its Financial Rules of Thumb—a free online resource designed to help young people understand marketing tactics, practice conscious spending, and build better financial habits.

“At Financial Basics Foundation we aim to empower young people with the life skills to think critically about marketing tactics and make informed spending choices,” Ms Samios said.

“By developing financial capability throughout their formative years, young people build strong financial habits that help set them up for a more secure financial future.”

The foundation’s resources are used in over 2,000 high schools across Australia, with more than 30,000 students engaging annually with its ESSI Money simulation game, which teaches core skills in earning, saving, and spending.

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The Financial Basics Foundation has raised concerns over the impact of increasingly sophisticated digital marketing tactics on young consumers during Financial Literacy Month, highlighting the need for stronger financial education to counter the influence of online retailers like Shein and Temu.

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ANZ is urging customers and the broader community to remain vigilant against scammers impersonating trusted organisations in the wake of Ex-Tropical Cyclone Alfred.

" ["fulltext"]=> string(3054) "

Scammers are known to exploit crises by posing as banks, insurance providers, charities, or disaster relief services to deceive individuals into revealing personal information or making fraudulent payments.

ANZ Head of Customer Protection, Shaq Johnson, warned that fraudsters often disguise themselves as recovery support services to manipulate those affected.

“Scammers can disguise themselves as recovery support, manipulating those affected by the crisis into sharing sensitive information or making fraudulent payments under false pretenses,” he said.

“Staying vigilant and taking simple precautions can help safeguard your recovery journey and prevent further setbacks during an already challenging time. Since scammers thrive on urgency and uncertainty, a thoughtful pause can be your strongest defence against fraudulent activity.”

How to spot a scam

ANZ advises customers to be cautious of unexpected requests for personal details and provides the following guidance:
- Verify the source – Be wary of calls, emails, or messages claiming to be from a bank or insurance company requesting urgent payments or unusual account activity. Confirm legitimacy by using official contact details from the organisation’s website.
- Think before you click – Legitimate organisations will never request personal information via a link in an email or text message.
- Be cautious of fake charity appeals – Always verify the authenticity of charity requests by checking their registration with the Australian Charities and Not-for-profits Commission (ACNC).
- Check for errors – Scams often contain poor grammar, spelling mistakes, or unnatural phrasing.

ANZ scam prevention measures

ANZ reminds customers that it will never:
- Request online banking passwords, PINs, ANZ Shield codes, or one-time passcodes.
- Ask customers to click on a link to log in or enter sensitive details.
- Request remote access to devices.
- Instruct customers to transfer funds to another account to keep them safe.
- Invite customers to join an online chat for security verification.

Reporting scams and seeking assistance

ANZ’s customer protection teams operate 24/7 to monitor scam activity. Customers who believe they may have been targeted should contact ANZ immediately at 13 33 50 or visit www.anz.com.au/security/report-fraud.

Further information on scam prevention is available at www.anz.com.au/security.

ANZ relief package for cyclone-affected customers

On 5 March 2025, ANZ activated a relief package for customers impacted by Ex-Tropical Cyclone Alfred. Customers experiencing financial hardship are encouraged to contact ANZ early for assistance. Those with ANZ Home and Contents Insurance may also be eligible for emergency support.

 

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• Yes

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ANZ is urging customers and the broader community to remain vigilant against scammers impersonating trusted organisations in the wake of Ex-Tropical Cyclone Alfred.

Write comment (0 Comments) " ["jcfields"]=> array(9) { [1]=> object(stdClass)#10934 (33) { ["id"]=> int(1) ["title"]=> string(17) "Automatic tagging" ["name"]=> string(17) "automatic-tagging" ["checked_out"]=> NULL ["checked_out_time"]=> NULL ["note"]=> string(0) "" ["state"]=> int(1) ["access"]=> int(1) ["created_time"]=> string(19) "2018-08-29 05:26:30" ["created_user_id"]=> int(2355) ["ordering"]=> int(-1) ["language"]=> string(1) "*" ["fieldparams"]=> object(Joomla\Registry\Registry)#10579 (3) { ["data":protected]=> object(stdClass)#10388 (1) { ["options"]=> object(stdClass)#10390 (2) { ["options0"]=> object(stdClass)#10391 (2) { ["name"]=> string(3) "Yes" ["value"]=> string(1) "1" } ["options1"]=> object(stdClass)#10389 (2) { ["name"]=> string(2) "No" ["value"]=> string(1) "0" } } } ["initialized":protected]=> bool(true) ["separator":protected]=> string(1) "." } ["params"]=> object(Joomla\Registry\Registry)#10578 (3) { ["data":protected]=> object(stdClass)#10386 (6) { ["hint"]=> 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