AI Scam Fears Are Rising—Here’s How Banks Respond

December is peak season for scams. Cards are swiped more often, pay‑in‑4 plans spike, parcels go missing, and inboxes fill with “delivery issue” texts. Add generative AI to that mix and you get a nasty upgrade: messages that read like they were written by a competent human, voices that sound like someone you know, and fake support chats that don’t trip the usual alarm bells.

Consumers are noticing. Even without access to the original article (the source page is blocked behind anti-bot security), the headline alone reflects what banks and fintech teams are hearing every day: people are increasingly worried about AI-powered scams—and those worries translate directly into hesitation about digital banking, real-time payments, and new fintech products.

Here’s my stance: the right response isn’t to slow AI down in finance—it’s to speed up AI adoption in fraud detection and consumer protection. The institutions that win trust in 2026 will be the ones that can say, with evidence, “We saw it, we stopped it, and we made it painless for you.”

Why AI scams feel “more real” (and why that matters)

AI scams work because they remove friction for criminals. The old model required effort: poor spelling, clunky scripts, obvious inconsistencies, and cheap audio. The new model produces convincing content at scale.

The three scam upgrades generative AI enables

AI makes scams faster, more personal, and harder to spot. In practice, that shows up as:

1. Polished impersonation: Fake emails and SMS messages that match a bank’s tone, formatting, and customer service language.
2. Deepfake voice and video: “It’s me, I need help” calls that mimic a family member, a CEO, or a bank agent.
3. Adaptive social engineering: Chat-based scams that respond convincingly to your skepticism instead of collapsing when you ask a detailed question.

This matters because financial fraud prevention has always depended on a gap: scammers were usually less believable than the real organisation. AI closes that gap. When consumers can’t rely on “vibes” to detect fraud, they need the institution to detect it for them.

Trust is now a measurable business metric

For banks and fintechs, rising scam concern isn’t just a reputational problem. It hits:

• Digital adoption (customers revert to branches/calls)
• Payment conversion (more abandoned transfers)
• Customer lifetime value (fear increases churn)
• Cost to serve (more inbound contact and disputes)

In the “AI in Finance and FinTech” series, we often talk about AI for credit scoring, personalisation, and automation. The reality is simpler: none of that matters if customers don’t feel safe using the product.

The better approach: fight AI scams with AI fraud detection

The most effective fraud programs now assume two things are true:

• Attackers are using AI.
• Rules-only defences will fall behind.

AI in fraud detection works best when it’s paired with strong controls and clear customer communication. You want models that catch patterns humans and static rules miss, and you want those models to operate in milliseconds.

What “AI fraud detection” should actually mean in 2025

A lot of vendors label basic automation as AI. The capabilities that reliably reduce scam losses are more specific:

• Behavioural biometrics: Detects unusual typing cadence, device handling, navigation patterns, and session anomalies.
• Graph-based detection: Maps relationships across accounts, devices, beneficiaries, IP ranges, mule networks, and merchants.
• Real-time transaction risk scoring: Scores each payment using context (device, history, payee age, velocity, geolocation, known scam markers).
• Natural language processing (NLP): Flags scam language patterns in inbound chats, emails, or customer-reported messages.
• Model orchestration: Uses multiple signals (not one “magic model”) with human-review routing for high-risk edge cases.

Snippet-worthy truth: Fraud detection isn’t a single model—it’s a decision system that combines identity, behaviour, network risk, and payment context.

Where Australian banks and fintechs are focusing

Australia’s move toward faster payments and digital-first banking raises the stakes: once money moves instantly, recall is harder. That pushes local institutions toward:

• Payee risk intelligence (new beneficiary + high value + urgency cues)
• Scam friction that’s targeted, not blanket (slow only suspicious flows)
• Step-up authentication (risk-based MFA, not “MFA for everything”)
• Cross-channel correlation (call centre + app + web activity viewed together)

The goal isn’t to block legitimate customers. It’s to intervene at the exact moment a scammer needs the customer to act quickly.

The scam moments that matter most (and how to stop them)

Banks and fintechs tend to over-index on the transaction itself. Scams often succeed earlier—during account takeover, payee setup, or social engineering.

Moment 1: Account opening and synthetic identity

Answer first: Stop synthetic identity fraud by combining document checks with device and network intelligence.

Document verification alone is no longer enough. Fraud rings reuse real documents, deepfake selfies, and rotated devices. Stronger defences include:

• Device fingerprinting and emulator detection
• Network signals (VPN, TOR, risky ASN)
• Consortium signals where available (shared mule/identity markers)
• ML-based anomaly scoring for application patterns

Moment 2: Account takeover (ATO) and session hijack

Answer first: Detect takeover by looking for behavioural breaks, not just bad passwords.

ATO often looks like “valid login + unusual behaviour.” Signals that work:

• New device + new location + new payee setup
• Password reset followed by rapid transfer attempts
• Changes to notification settings (a classic pre-theft move)

A pragmatic control stack is:

1. Real-time risk score at login
2. Step-up auth for risky sessions
3. Temporary limits for new payees when risk is high
4. Fast customer confirmation inside the trusted app channel

Moment 3: Authorised push payment scams (customers approve it)

Answer first: Use AI to detect coercion patterns and interrupt the scam script.

The hardest category is when the customer is tricked into sending money. The customer is “authenticated,” so old-school fraud logic says it’s fine.

This is where AI earns its keep. You can detect:

• First-time payment to a new beneficiary with high urgency
• Rapid escalation in payment size
• Copy/paste patterns into payee fields (scam instructions)
• Customer behaviour that suggests stress or confusion (navigation loops, repeated failed actions)

Then intervene with smart friction, such as:

• A confirmation screen that names the scam type (“investment scam,” “ATO recovery scam,” “ATO phone impersonation”) in plain language
• A short delay for high-risk transfers, with in-app verification
• A one-tap “I think I’m being scammed” button that routes to a specialised team

Here’s what works: don’t ask customers “Are you sure?” Ask them something that breaks the script. For example: “Has someone told you to keep this transfer secret?”

Practical checklist: what to implement in the next 90 days

If you run fraud, risk, product, or operations at a bank or fintech, the quickest wins aren’t flashy. They’re operationally tight.

1) Build a unified scam signal layer

Answer first: Centralise your signals so models can see the full story.

Unify (even if imperfectly) these streams:

• Login and session telemetry
• Device intelligence
• Payee lifecycle events
• Payments and limits
• Contact centre interactions and outcomes
• Chargebacks and disputes

AI models underperform when each channel is blind.

2) Move from “rules vs AI” to “rules + AI”

Answer first: Keep deterministic controls for known bad, use ML for unknown bad.

• Rules still shine for blacklisted entities, impossible geographies, and compliance thresholds.
• ML shines for subtle patterns, evolving scams, and network effects.

The best systems treat rules as guardrails and ML as early warning.

3) Create an intervention playbook your teams can run

Answer first: Decide ahead of time what happens when risk is high.

Define actions by risk tier:

• Low risk: allow
• Medium: step-up auth
• High: hold payment, in-app verification, specialist review
• Critical: block and freeze, rapid customer outreach

Operational clarity reduces losses and customer frustration.

4) Measure the metrics that actually change outcomes

Answer first: Track prevention and customer impact side by side.

At minimum:

• Scam loss rate (per 1,000 customers and per $ volume)
• False positive rate (blocked legitimate payments)
• Time-to-intervene (milliseconds to decision; minutes to customer contact)
• Recovery rate (what you claw back)
• Repeat victimisation rate

If you’re not measuring repeat victimisation, you’re missing the most painful part of scam harm.

“People also ask” (the questions your customers are thinking)

Are AI scams mostly deepfakes?

No. Deepfakes get headlines, but most AI scams are better-written messages and smarter chat scripts. They scale cheaply and convert well.

Can banks detect scams if customers authorise the transfer?

Yes—if the bank treats scams as a behavioural and contextual risk problem, not just an authentication problem. Authorised doesn’t mean safe.

Will stronger fraud controls hurt user experience?

Badly designed controls will. Risk-based controls don’t have to. The target is less friction for good customers and more friction exactly where scammers operate.

The trust dividend: why investing in AI safety pays back

Consumers’ concern about AI scams is rational. The fraud toolset has improved dramatically, and criminals are quick learners. But the same technology that produces convincing scam content also produces better detection—especially when banks and fintechs invest in:

• Real-time risk scoring
• Network/graph intelligence
• Behavioural signals
• Fast, human-friendly interventions

I’ve found that the institutions that gain trust don’t just stop fraud; they communicate safety clearly. They explain why a payment is being held, they offer a quick path to proceed safely, and they treat scam victims like customers who need help—not like liabilities.

If you’re building in the “AI in Finance and FinTech” space, this is the moment to prioritise protection. AI personalisation and smarter credit models are great, but trust is the feature that keeps customers using them.

Where do you want your organisation to land in 2026: explaining why scams got through, or proving you stopped them before customers lost money?