AI vs Deepfakes: Build a Crisis-Ready Finance Culture

Deepfakes used to be a party trick. Now they’re a financial control problem.

Across banking and fintech, the hardest incidents to manage aren’t always the most technical—they’re the ones that unfold fast, cross channels (phone, app, branch, social), and pressure teams into making “just this once” exceptions. Downtime hits that same nerve: customers can forgive a brief outage, but they won’t forgive confusion, silence, or a messy recovery.

The catch: the source article behind this post (an AFP session featuring leaders from BMO and Affirm) was blocked behind anti-bot protection when scraped. So rather than pretending we saw the full transcript, I’m going to use the theme—deepfakes, downtime, and the demand for a crisis-ready culture—to give Australian banks and fintechs a practical, AI-forward playbook that actually holds up in the real world.

Deepfakes are a fraud problem—and a people problem

Deepfakes succeed because they exploit trust and urgency, not because they’re perfect.

Most financial losses happen when a fake identity or synthetic voice pushes a human (or a brittle process) into bypassing controls: approving a payment, resetting credentials, changing a phone number, or disclosing sensitive information. The technical forgery is only half the attack; the rest is social engineering under time pressure.

Where deepfakes hit Australian financial services hardest

In the “AI in Finance and FinTech” series, we talk a lot about AI for fraud detection and credit scoring. Deepfakes sit at the intersection of fraud, identity, and operational resilience because they can trigger cascading failures:

• Account takeover (ATO) via deepfake voice calls to contact centres (“I lost my phone, I need a reset now”).
• Payment redirection scams where a convincing executive video/voice requests an urgent transfer.
• Remote onboarding abuse using synthetic faces or replay attacks to pass selfie checks.
• Vendor and partner compromise where deepfakes impersonate a known counterparty to change bank details.

The uncomfortable truth: if your controls depend on “we know our customers’ voices” or “staff can tell when something feels off,” you’re already behind.

What AI can do that humans can’t

AI doesn’t “solve” deepfakes by spotting a single tell. It wins by correlating weak signals at speed:

• Device and network anomalies (new device, unusual location, suspicious IP reputation)
• Behavioural biometrics (typing rhythm, navigation patterns, gesture signatures)
• Session risk scoring (velocity of changes, abnormal sequences, time-of-day patterns)
• Cross-channel correlation (a password reset + new payee + high-value transfer within 20 minutes)

This matters because deepfakes are getting cheaper and more accessible. Your defence has to be systemic—a layered model that assumes any single factor (face, voice, OTP, even staff judgement) can fail.

AI-driven fraud detection needs a “crisis posture,” not a dashboard

A crisis-ready culture is visible in one moment: when something weird happens at scale, teams don’t freeze.

Many organisations buy tools and then treat incidents as exceptions. The reality is simpler: in modern finance, incidents are part of operations. If you’re using AI in finance for fraud detection or real-time monitoring, you should design the program as if you’ll be tested—often.

The resilience mindset: detect, decide, communicate

When deepfake-driven fraud or major downtime occurs, three things must happen quickly:

1. Detect: Identify abnormal patterns before customers report them.
2. Decide: Choose safe actions with incomplete information.
3. Communicate: Tell customers and staff what’s happening in plain language.

AI helps most in step 1, but the failure mode is step 2: teams hesitate, escalate endlessly, or default to risky “business as usual.” A crisis-ready culture pre-approves decision paths.

A useful rule: if an incident requires three leadership approvals to contain, it will spread faster than you can approve it.

A practical “AI incident” decision matrix

Create a matrix that maps risk signals to automatic actions, with clear ownership:

• High-confidence ATO indicators → lock session, step-up auth, freeze payee addition
• Suspicious payee changes → 24-hour cooling-off for first payment, notify customer
• Deepfake voice suspicion in contact centre → switch to out-of-band verification, limit account changes
• Platform instability or downtime → fail safe (block risky actions), degrade gracefully (read-only), proactive status updates

This isn’t theoretical. It’s how you stop a deepfake event from becoming a headline.

Building deepfake resilience: controls that actually work

The best deepfake strategy is to make impersonation less profitable.

If a criminal can’t move money quickly, can’t change credentials without friction, and can’t add a payee without visibility, the deepfake becomes noise—not a breach.

Step-up authentication that doesn’t punish everyone

Australian customers are already tired of clunky verification. The goal is risk-based friction:

• Low-risk activity: keep it fast
• Medium-risk: silent checks (device binding, behavioural biometrics)
• High-risk: strong step-up (passkeys, in-app confirmation, biometric re-auth)

If you’re still leaning heavily on SMS one-time passwords for high-risk actions, you’re betting against SIM swap and social engineering.

Treat contact centres as a high-risk channel

Deepfake voice scams target the contact centre because it’s built for speed and empathy. You don’t want to strip that away—but you must redesign it.

Controls that work in practice:

• Out-of-band confirmation for sensitive changes (in-app approve/deny)
• Verifiable call-back flows (customer requests a call in-app; you call them)
• Agent tooling that shows an AI risk score and the reasons (recent failed logins, device mismatch, unusual payee activity)
• Hard limits during suspicion (no email change + no phone change + no payee add in same call)

Here’s the stance I’ll take: if your fraud team can see session risk but your agents can’t, you’re leaving your staff exposed.

Deepfake detection: useful, but not enough

Yes, you can deploy deepfake detection models for video and voice. They help. But attackers adapt.

The durable approach is to combine:

• Media forensics (liveness, replay detection, artifact detection)
• Identity graph analytics (synthetic identity clusters, shared devices, shared bank accounts)
• Transaction anomaly detection (first-time behaviours, unusual merchant/payee patterns)
• Controls on money movement (cooling-off periods, confirmations, dynamic limits)

Deepfake detection should be a layer, not the layer.

Downtime is a fraud amplifier—plan for it like a security event

Downtime doesn’t just hurt revenue. It creates confusion and opportunity.

When customers can’t access balances, transfers are delayed, or alerts aren’t delivered, criminals exploit the gap: fake “support” numbers, phishing “status updates,” impersonated bank staff, and social engineering that feels plausible because the platform is unstable.

What “crisis-ready culture” looks like during an outage

A resilient organisation does three things immediately:

• Stabilises the blast radius: temporarily restrict high-risk functions (new payees, limit increases) rather than trying to keep everything running.
• Keeps a single source of truth: one internal incident channel, one customer message stream, one decision owner per domain.
• Communicates early and plainly: what’s affected, what’s safe to do, what customers should ignore.

That last point is where many teams hesitate. But silence is a vacuum scammers fill.

AI for operational resilience: beyond alerts

AI in finance is often sold as efficiency. For resilience, its value is different: early warning and triage.

Practical uses that hold up under pressure:

• Anomaly detection on service performance (error spikes, latency drift) tied to customer impact
• Automated incident clustering (grouping similar failures across microservices)
• Runbook assistance (surfacing likely causes and next checks based on past incidents)
• Customer contact deflection that doesn’t lie (AI assistants that can state known impacts and safe alternatives)

If you do this well, you reduce time-to-diagnosis and prevent the “secondary incident” of fraud that rides on top of downtime.

A crisis-ready culture: the part technology can’t buy

Tools don’t create culture. Incentives and rehearsal do.

A crisis-ready culture shows up when teams treat fraud, deepfakes, and outages as connected risks—because they are. Here’s what I’ve seen work best when financial services organisations want operational resilience without theatre.

Rehearse the incidents you’re avoiding

Run short, realistic exercises quarterly:

• Deepfake voice call + urgent payee change + partial outage
• Synthetic identity onboarding spike + fast-follow credit application fraud
• Social media impersonation wave during degraded service

Keep them cross-functional: fraud, security, contact centre, comms, product, engineering, and legal. Measure speed and clarity, not perfection.

Define “safety rails” that nobody can override casually

Crisis culture dies when exceptions become normal.

Set non-negotiables like:

• No high-value first-time payments without confirmation
• No credential resets without out-of-band verification
• No simultaneous change of email + phone + device binding
• Cooling-off periods for risky profile changes

If leadership wants a bypass, it should require a logged decision and after-action review.

Make AI explainable enough for humans to act

If a model’s output is a score with no explanation, it will be ignored at the worst possible moment.

Your frontline teams need:

• The top 3 reasons for the risk score (clear language)
• The approved action to take (script + workflow)
• The customer-safe explanation (so staff can communicate without panic)

This is where AI risk management meets training. Models don’t reduce incidents if people don’t trust them.

“People also ask” (and what I tell teams)

Can banks really detect deepfakes reliably?

Banks can detect some deepfakes, but reliability comes from layered controls—risk scoring, liveness checks, identity graphs, and transaction controls—not a single detector.

What’s the fastest win for deepfake fraud prevention?

Put strong step-up authentication and cooling-off controls around payee changes and first-time payments. It reduces loss even when the impersonation looks convincing.

How does AI help with crisis preparedness in finance?

AI improves early detection, triage, and consistent customer communication during incidents. The cultural win is faster decisions with fewer ad-hoc exceptions.

What to do next (if you want fewer nasty surprises in 2026)

Deepfakes and downtime aren’t separate problems. They’re both stress tests of trust, controls, and coordination. The organisations that handle them well don’t rely on heroics—they build systems that assume pressure, ambiguity, and adversaries.

If you’re building out AI-driven fraud detection or operational resilience in an Australian bank or fintech, start with two moves: risk-based friction for high-risk actions and incident playbooks that connect fraud + uptime + comms. Those two alone reduce real losses.

Where do you think your organisation would break first: the model, the process, or the moment someone senior asks for “just a quick exception”?