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.