AI Scam Risks: How Aussie Finance Can Respond

Consumer anxiety about AI scams is rising for a simple reason: the scams are getting better faster than most bank controls and customer habits are improving.

Over the last 18 months, I’ve watched the “classic” fraud playbook (phishing emails, basic impersonation, clumsy fake invoices) evolve into something more convincing: natural-sounding voice calls, personalised messages that reference real details, and fake documents that look like they came straight from a bank portal. For Australian banks and fintechs, this isn’t just a fraud-loss problem. It’s a trust problem, and trust is a growth lever.

This post sits within our AI in Finance and FinTech series, and it takes a clear stance: if scammers are using AI to scale deception, financial institutions have to use AI to scale protection—plus they have to teach customers what protection looks like in 2025.

Why consumers are more worried about AI scams

Consumers are increasingly concerned about AI scams because the “tell” is disappearing. The grammar mistakes, the odd timing, the robotic cadence—those were cues people relied on. Modern generative AI erases many of them.

Two dynamics are making the fear feel rational:

1. AI increases scam quality: Messages are coherent, context-aware, and tailored to the channel (SMS, email, social, voice). That boosts conversion rates because more people believe the interaction is real.
2. AI increases scam volume: The marginal cost of creating a convincing variant is close to zero. Scammers can test thousands of messages, learn what works, and iterate like a growth team.

For financial services, the consequence is brutal: even if your fraud controls hold steady, customers experience more “near misses,” more account lockouts, more confusing warnings—and they start to feel unsafe.

The new reality: fraud is now a product team

Scam operations are running A/B tests. They’re using scripted LLM workflows to:

• write believable outreach at scale
• generate call centre-style scripts for live scammers
• craft “bank-like” explanations when challenged
• produce realistic PDFs (statements, invoices, ID scans)

That’s why consumer concern is rising. People aren’t imagining things. The fraud experience is objectively more persuasive than it used to be.

What AI-driven scams look like in Australian financial services

AI scams aren’t one category. They’re a set of techniques that wrap around existing fraud types—authorized push payment (APP) fraud, account takeover, identity fraud, and invoice redirection.

Here are the patterns showing up most often in banking and fintech contexts.

Deepfake voice and executive impersonation

The direct answer: voice cloning increases the success rate of impersonation because it bypasses a customer’s “this feels wrong” instinct.

Common scenarios include:

• A customer gets a call that sounds like their bank’s fraud team, complete with a believable script and urgency.
• A business receives a call “from the CFO” to approve a same-day payment to a “new supplier account.”

The play is always the same: compress the decision window so the victim doesn’t verify via a second channel.

Hyper-personalised phishing and SMS (smishing)

The direct answer: LLMs produce personalised messages that look legitimate across channels, especially SMS where short, confident language can feel “official.”

Scammers can combine breached data (names, addresses, partial card numbers) with AI-written messages to create a convincing pretext:

• “We’ve detected unusual activity on your account ending in 42.”
• “Your payment is on hold—verify in the next 30 minutes.”

Even financially savvy users get caught because the message matches real-life patterns.

Synthetic identities and faster onboarding abuse

The direct answer: AI lowers the effort required to create consistent fake identity artifacts, which stresses digital onboarding controls.

Synthetic identity fraud doesn’t always need deepfakes. It often relies on plausible-but-false combinations of:

• altered documents
• AI-generated selfies that pass basic liveness checks
• fabricated employment or income evidence

This ties directly to our broader series theme: AI in finance isn’t only about customer experience and faster approvals; it has to be balanced against risk management.

The flip side: AI is also your best defence (if you deploy it properly)

Banks and fintechs should use AI for fraud detection because rules alone can’t keep up with the speed and variation of AI scams.

That doesn’t mean “buy an AI tool.” It means building a layered system where models, rules, and human review reinforce each other.

Behavioural analytics beats “one weird trick” detection

The direct answer: fraud detection performs better when it models behaviour, not just indicators.

AI scams often succeed without malware or obvious technical compromise—especially in APP fraud, where the customer authorises the payment.

Behavioural analytics can flag:

• first-time payees with high-risk patterns
• unusual session navigation (jumping directly to payments screens)
• typing cadence changes and device posture shifts
• repeated failed verification attempts followed by a successful one

This is where Australian banks and fintechs can win: focus on the shape of activity, not just the content of a message.

Risk-based friction: make it harder only when it’s risky

The direct answer: customers tolerate friction when it’s targeted and explained.

A common mistake is adding blanket controls after a fraud spike—extra OTPs for everyone, more step-ups, more lockouts. That reduces conversion and annoys legitimate users.

Better approach: apply step-up controls only when risk signals stack up. Examples:

• A payment to a new payee + unusual device + urgent transfer amount = require out-of-band verification
• A new device + password reset + payee creation = slow the flow and trigger assisted verification

The goal is simple: let normal customers move fast, force scammers to slow down.

GenAI for the defenders (yes, really)

The direct answer: GenAI improves fraud operations when it’s used to speed analysis, not automate decisions blindly.

Practical uses that work well:

• summarising fraud case notes for investigators
• clustering similar scam reports to spot campaigns earlier
• drafting customer-facing warnings in plain English
• generating “what changed?” explanations for analysts when a model score spikes

Used this way, GenAI increases throughput and consistency without turning your fraud team into a black-box model babysitting crew.

The trust gap: education is part of fraud prevention

Consumer concern about AI scams won’t drop just because your detection models improve. People judge safety by what they experience: clarity, speed, and how supported they feel when something looks off.

Australian financial institutions should treat customer education as a product, not a PDF.

What to teach customers (and how to teach it without boring them)

The direct answer: the best education is specific, repeated, and delivered at the moment of risk.

Instead of generic “beware of scams” messaging, focus on concrete behaviours:

1. Verification habits
   • “We will never ask you to move money to a ‘safe account.’”
   • “Hang up and call back using the number in your app.”
2. Payee hygiene
   • “New payee? Pause. Confirm via a second channel.”
   • “Invoice change? Verify bank details with a known contact.”
3. Time-pressure awareness
   • “Urgency is a tactic. Real banks can wait for verification.”

Make it real with short in-app prompts at the exact time of risk: payee creation, large transfers, address changes, password resets.

A scam warning that appears three seconds before a transfer is more valuable than a brochure nobody reads.

Better customer experiences during fraud controls

The direct answer: good fraud UX reduces abandonment and increases trust.

If you block a transaction, tell customers what to do next in plain language:

• what triggered the block (high-level, not a model dump)
• what verification steps are available
• how long it will take
• what the bank will never ask for

This is where fintechs often outperform incumbents—fast, clear flows. Banks can match that by treating fraud journeys like conversion funnels.

A practical 90-day plan for banks and fintechs

If your team is staring at a backlog of fraud initiatives, here’s a focused plan that improves outcomes quickly without pretending you can solve everything at once.

Days 0–30: Measure what’s actually happening

The direct answer: you can’t manage AI scam risk without clean labels and consistent taxonomy.

• Standardise scam categories (APP vs takeover vs identity) and sub-types (impersonation, invoice redirection, romance, investment).
• Add structured fields to case notes so patterns can be analysed.
• Track customer-reported “near misses” as a leading indicator, not noise.

Days 31–60: Deploy targeted controls where loss concentrates

The direct answer: fraud improvements compound when you start at the highest-loss journey.

• Add risk-based step-up on new payees and high-value transfers.
• Introduce payee confirmation patterns for business banking flows.
• Tighten device-binding and session risk scoring (especially around credential resets).

Days 61–90: Reduce scam conversion with education at the moment of truth

The direct answer: you’ll prevent more fraud by interrupting the scam script than by writing better post-fraud comms.

• Add in-app “call-back verification” prompts when a transfer looks coached.
• Build a one-tap “I’m on a call and unsure” pathway to a verified support channel.
• Train frontline staff to recognise AI-scam patterns and coach customers through verification.

Where this fits in the broader AI in Finance and FinTech story

AI in finance is often sold as speed: faster onboarding, smarter credit scoring, better personalisation, sharper risk models. I’m pro-speed—until speed becomes the attacker’s advantage.

The institutions that win in 2026 will be the ones that treat AI fraud detection, risk management, and customer trust as one system. If a customer believes your app is the safest place to move money, you don’t just reduce losses—you earn deposits, retention, and referrals.

If you’re leading fraud, product, or risk at an Australian bank or fintech, the question to ask your team this week is simple: where are we still relying on customers to spot scams that AI has already made believable?