AI Crisis-Ready Culture: Deepfakes, Downtime, Trust

Deepfakes and outages hit differently in financial services: they don’t just cause operational pain—they trigger instant credibility loss. One convincing fake “CEO voice note” can move money faster than a fraud team can open a ticket. One hour of downtime can undo a year of brand-building.

The frustrating part is that most organisations still treat these events as separate problems. Fraud is “security’s issue.” Downtime is “tech’s issue.” Customer panic is “comms’ issue.” That siloed thinking is exactly why incidents spiral.

This post is part of our AI in Finance and FinTech series, and I’m going to be blunt: a crisis-ready culture is now a baseline requirement. AI can help a lot—especially for deepfake detection, fraud prevention, and real-time response—but only if the organisation is set up to act on what AI finds.

Deepfake fraud is now a crisis scenario, not a novelty

Deepfakes have crossed the line from “interesting tech demo” to repeatable fraud technique. What makes deepfakes so dangerous for banks and fintechs isn’t just realism—it’s speed and scale. A synthetic voice call can pressure a staff member. A manipulated video can trigger customer runs. A fake identity can slip through onboarding if your controls rely on static checks.

In Australia (and globally), the pattern is consistent: attackers combine social engineering + AI-generated media + fast payment rails. If your institution supports instant payments, you’ve already shortened the window for human intervention.

Where deepfakes hit hardest in finance

The most common high-impact entry points are operational—not exotic:

• Authorised push payment (APP) fraud: convincing audio/video used to “confirm” urgency or authority
• Account takeover: synthetic identity signals plus phishing, then “voice verification” bypass attempts
• Synthetic identity onboarding: face swaps, AI-edited documents, and stolen attributes stitched together
• Internal approval scams: fake exec instructions targeting treasury ops, vendor changes, or “emergency transfers”

Here’s the cultural failure mode: teams wait for “proof” something is fake. By the time proof arrives, the money’s gone.

How AI helps (when you use it correctly)

AI is a strong countermeasure when it’s positioned as decision support and paired with clear playbooks.

Practical AI-enabled controls that work in production:

1. Liveness detection beyond the selfie

   • Use challenge-response and passive signals (micro-movements, texture artifacts, depth cues)
   • Rotate challenges so attackers can’t rehearse

2. Multi-modal fraud detection

   • Combine behavioural biometrics (typing, swiping), device intelligence, network signals, and transaction patterns
   • Treat “media” (voice/video) as one input, not the source of truth

3. Voice and media anomaly models

   • Detect artifacts like spectral inconsistencies and compression patterns
   • Flag high-risk interactions for step-up verification rather than blocking everything

4. Graph-based risk scoring

   • Link entities (devices, accounts, payees, IPs, merchants) to surface mule networks and reused infrastructure

The stance I recommend: assume deepfake attempts will get past at least one layer. Build controls that catch the attack in the next layer—before funds leave, or before settlement becomes irreversible.

Downtime costs trust more than most postmortems admit

Downtime is often reported as a technical event: “API latency increased,” “database failover delayed,” “third-party outage.” Customers experience something simpler: “My money isn’t accessible.”

That gap matters. In finance, reliability is part of the product. If your app fails during a salary run, a holiday shopping weekend, or a market-moving event, customer trust takes a direct hit.

Late December is a good reminder: usage spikes around holidays, travel, and end-of-year reconciliation. When systems wobble during peak periods, customers don’t care about root causes—they care that you weren’t ready.

Why outages and fraud are connected

Attackers love chaos. Outages create:

• Noise (alerts and queues overflow)
• Workarounds (“just approve it manually”)
• Weakened controls (temporary bypasses, relaxed thresholds)
• Customer vulnerability (more likely to click “support” scams)

A crisis-ready culture treats uptime and fraud defence as mutually reinforcing. Resilience is a fraud control.

How AI improves operational resilience

AI won’t replace good engineering, but it can tighten detection and response:

• AIOps anomaly detection: spot unusual error rates, memory patterns, queue backlogs, and dependency failures faster than static thresholds
• Incident clustering and blast-radius prediction: correlate seemingly unrelated alerts into one coherent incident and forecast impacted services
• Automated triage: suggest likely root causes and remediation steps based on past incident patterns
• Load and capacity forecasting: predict peak load windows and resource contention before customers feel it

The key is integration: these signals must route into your incident command process, not just sit in dashboards.

“Crisis-ready culture” is a set of behaviours, not a poster

Most companies get this wrong. They buy tools, run a tabletop exercise once a year, and assume they’re covered. A crisis-ready culture is visible in the first 30 minutes of a live incident.

Here’s what it looks like in banks and fintechs that handle pressure well:

Clear decision rights under pressure

When fraud and downtime hit at the same time, indecision is expensive. You need explicit answers to:

• Who can pause certain payment flows?
• Who can trigger step-up verification across channels?
• Who can throttle onboarding or risky transaction types?
• Who approves customer messaging and timing?

If those decisions require three committees, you’re not crisis-ready.

Playbooks that connect fraud, ops, and comms

Your playbooks should include both technical actions and customer-facing moves. For example, a deepfake-driven APP fraud spike during partial downtime might require:

• Raising risk thresholds for first-time payees
• Temporary holds on high-risk outbound transfers above a set amount
• In-app banners warning about impersonation scams (written in plain language)
• Enhanced monitoring for contact-centre scripts (attackers may call pretending to “help”)

A useful playbook includes “if-then” triggers tied to measurable signals (volume spikes, new payee rate, call-centre pattern changes).

Training that matches real attacker behaviour

Deepfake defence fails when training is too generic (“be careful with unusual requests”). Make it specific:

• Teach staff to treat voice/video as untrusted for high-risk approvals
• Require a separate channel confirmation (known-good number, secure internal chat, or approval workflow)
• Drill scenarios where the attacker uses urgency, authority, and plausible context

I’ve found that the best exercises are short and frequent: 30 minutes monthly beats a three-hour annual workshop.

A practical AI crisis-readiness blueprint (90 days)

You can make measurable progress in a quarter without boiling the ocean. The goal is to reduce two things: time-to-detect and time-to-act.

Days 1–30: Map the failure paths

Answer-first: identify where a deepfake or outage becomes a loss.

• List your top 10 “irreversible” actions (high-value transfers, payee changes, password resets, limit increases)
• For each, map:
  • signals you already capture (device, behaviour, transaction)
  • where decisions happen (rules engine, human queue, contact centre)
  • how quickly funds settle (your real intervention window)
• Define a simple crisis severity model (SEV1–SEV3) shared by security and engineering

Deliverable: a single-page “crisis map” that shows who does what in the first hour.

Days 31–60: Add AI where it changes outcomes

Answer-first: focus AI on high-leverage detection and prioritisation.

• Implement or tune real-time transaction risk scoring with step-up actions
• Add behavioural biometrics for high-risk journeys (new device + payee creation + transfer)
• Deploy anomaly detection for operational telemetry (AIOps) tied to paging
• Start entity resolution / graph linking to connect suspicious clusters

Deliverable: measurable reductions in false positives and faster escalation of true risk.

Days 61–90: Operationalise with playbooks and drills

Answer-first: automation without action plans is just noise.

• Create 3 combined playbooks:
  1. Deepfake impersonation + payment fraud surge
  2. Partial outage + increased scam attempts
  3. Third-party disruption + customer verification overload
• Run monthly drills with fraud, SRE/ops, product, and comms together
• Add a “kill switch” policy with guardrails (what can be paused, for how long, who approves)

Deliverable: reduced mean time to respond (MTTR) and fewer ad-hoc control bypasses.

A strong rule for finance teams: If your controls depend on everyone staying calm, they’ll fail when it matters.

Common questions leaders ask (and straight answers)

“Won’t AI increase friction for customers?”

Not if you use it for precision. The point is to step up verification for the small slice of sessions that look wrong—new device, unusual payee, abnormal behaviour—not for everyone.

“Can we just buy a deepfake detection tool?”

You can buy detection, but you can’t buy response. Tools help. Culture and playbooks stop losses. If your process can’t act quickly (holds, step-ups, outbound limits), detection becomes a report you read after the fact.

“What should we measure to prove progress?”

Track outcome metrics tied to real risk:

• Time-to-detect for impersonation and APP fraud spikes
• Time-to-contain (how fast you can apply step-up or holds)
• Fraud loss rate on high-risk flows (new payees, first transfer)
• Downtime minutes that affect “money movement” journeys
• False positive rate and manual review queue age

Build crisis readiness like it’s part of the product

Deepfakes, downtime, and crisis response aren’t separate topics anymore. They’re one operating reality: financial services now runs in an adversarial environment, at always-on speed.

If you’re building in the AI in Finance and FinTech space—bank, lender, payments provider, or wealth platform—treat crisis readiness as product work. Put it on the roadmap. Fund it. Drill it. Then use AI where it meaningfully improves detection, prioritisation, and response.

If you had to choose one next step this week, make it this: pick a single high-risk journey (new payee + first transfer is a classic) and design the AI signals + human decisions + customer messaging as one system. That’s where a crisis-ready culture stops being a slogan and starts being an advantage.

What would break first in your organisation: your systems, your controls, or your decision-making speed?