AI Security Collaboration: A Practical Playbook

Financial services has a security paradox: the more digital and AI-driven your systems become, the more you depend on other organisations to keep them safe. Banks can harden their own networks, but fraud flows across payment rails, mule networks span multiple institutions, and data breaches rarely stop at the edge of one vendor.

That’s why “collaboration” isn’t a feel-good slogan in cybersecurity anymore. In Australian banking and fintech, it’s a risk control. And as AI becomes central to fraud detection, credit decisioning, and customer support, collaboration becomes the difference between AI that protects you—and AI that creates new attack paths.

This post is part of our AI in Finance and FinTech series. The focus here is practical: what cross-sector security collaboration looks like in 2025, where AI helps (and where it can hurt), and the steps leaders can take to turn “we should partner more” into measurable outcomes.

Collaboration is a security control (not a committee)

The most useful way to think about collaboration is simple: it reduces the time between “signal” and “action.” If a fraud pattern shows up at one bank at 9:05am, the value of that insight collapses if other banks learn about it at 3:00pm.

In finance, the attack surface is shared:

• Payment ecosystems: card schemes, NPP-style rails, wallets, processors, and merchants all see different parts of the same fraud chain.
• Identity ecosystems: telcos, device intelligence providers, document verification vendors, and government ID services each hold partial truths.
• Software supply chains: one compromise in a common vendor can ripple through dozens of institutions.

AI raises the stakes further. Model-driven decisions happen at machine speed, so defence also has to move at machine speed, or you’ll always be reacting.

Where most organisations get it wrong

Many “collaboration” efforts fail because they’re framed as relationship building rather than operational capability. The typical pitfalls:

1. Information sharing without actionability: generic alerts, no context, no consistent formats.
2. Legal and privacy paralysis: teams assume “we can’t share anything” instead of designing safe sharing.
3. Vendor-heavy reliance: outsourcing the thinking to tools that don’t align to your threat model.

The better framing: treat collaboration like you treat uptime or liquidity risk—with clear service levels, governance, and accountability.

AI makes partnership security harder—and more necessary

AI improves detection, but it also creates new failure modes. If you’re using AI in fintech, you’re inheriting new classes of security risk that no single firm can solve alone.

New attack patterns AI teams must plan for

AI systems don’t just get “hacked.” They get manipulated.

• Synthetic identity and deepfake onboarding: Generative AI lowers the cost of creating believable IDs, videos, and proof-of-life artefacts. Fraudsters iterate quickly.
• Adversarial fraud: Criminals probe your model with many small attempts to learn decision boundaries (what gets approved vs blocked).
• Data poisoning: If your model learns from operational data (chargebacks, disputes, flagged transactions), attackers try to contaminate labels.
• LLM prompt attacks in customer support: If AI agents can access account data or initiate workflows, prompt injection becomes a real operational threat.

Here’s the part that’s unpopular but true: AI security isn’t a “your model” problem; it’s an ecosystem problem. Deepfake onboarding, for example, is easiest to counter when banks, fintechs, identity providers, and telcos share signals about known templates, mule accounts, and device farms.

AI collaboration advantage: shared learning at scale

The strongest collaboration pattern for AI security is shared learning without shared raw data. In practice, that can include:

• Federated learning or privacy-preserving analytics for fraud models
• Shared feature standards (e.g., consistent device, behavioural, and network features)
• Cross-institution watchlists for mule accounts, compromised devices, or scam payment destinations (with tight governance)

The result isn’t just better detection. It’s faster model adaptation when attackers change tactics.

What “good” collaboration looks like in Australian banking and fintech

Effective collaboration has three layers: operational, technical, and governance. Miss one and the whole thing becomes theatre.

1) Operational collaboration: align on playbooks and speed

Start by agreeing on the moments where speed matters. In my experience, these are the big ones:

• Account takeover containment (minutes matter)
• Scam payment disruption (hours matter)
• New fraud typology detection (days matter)

Then operationalise it:

• Shared incident categories and severity levels
• Clear escalation paths across institutions
• Agreed turnaround times for critical intel
• Tabletop exercises that include partners (banks, fintechs, key vendors)

Collaboration is only real when you can name the person who picks up the phone—and the exact decision they’re authorised to make.

2) Technical collaboration: make data interoperable

Security teams often “share” intelligence by emailing PDFs or dumping alerts into portals nobody checks. AI needs something better.

Aim for:

• Machine-readable signals (structured fields, consistent identifiers)
• Common event schemas for fraud and cybersecurity telemetry
• Confidence scores and provenance: where did this signal come from, and how reliable is it?

If you’re building AI fraud detection, interoperability affects outcomes directly. A cross-bank scam destination list is useless if each organisation formats payee identifiers differently or can’t map merchant descriptors consistently.

3) Governance collaboration: design privacy and trust into the system

For Australian financial institutions, collaboration must be designed around privacy, consumer protection, and operational risk. The trick is to stop treating privacy as a blocker and treat it as a design requirement.

Practical governance patterns that work:

• Data minimisation by default: share signals, not full customer records
• Purpose limitation: narrow, documented use cases (e.g., scam disruption)
• Retention limits: don’t keep shared data forever “just in case”
• Redress and dispute pathways: what happens when a signal is wrong?

Security collaboration collapses when partners worry they’ll be blamed for sharing. Strong governance makes sharing safer.

The AI-enabled partnership toolkit (you can start this quarter)

If you want an actionable path, focus on a small number of high-impact “shared assets” and build from there.

Shared asset #1: scam and mule network intelligence

Scams spike seasonally—December is a favourite period for social engineering because volumes are high and attention is low. Instead of each institution fighting alone, build a shared capability around:

• Mule account indicators (rapid inbound/outbound, new payees, high-risk corridors)
• Beneficiary risk scoring (payee, BSB/account patterns, account age signals)
• Device and behavioural anomalies linked to scam coaching

AI contributes by correlating weak signals across channels: device fingerprints, typing cadence, session anomalies, and payee history.

Shared asset #2: model-risk signals (what attackers are testing)

Most firms track fraud outcomes. Fewer track model probing behaviour. Yet it’s one of the clearest indicators of adversarial activity.

Share aggregated signals like:

• Unusual bursts of small-amount transactions across many accounts
• High-volume onboarding attempts from related device clusters
• Repeated “near-threshold” applications (credit or KYC) with slight variations

This is where collaboration really pays: one fintech might see the probing first; a major bank might see the monetisation. Together you see the full funnel.

Shared asset #3: vendor and supply-chain telemetry

Fintech ecosystems run on shared providers: onboarding, cloud, comms, analytics, and customer support platforms.

A pragmatic collaboration step: treat critical vendors as shared infrastructure and coordinate on:

• Minimum security baselines
• Shared incident notification requirements
• Joint “blast radius” analysis exercises

If a vendor is compromised, the speed of coordinated response often matters more than the sophistication of any single control.

“People also ask” (and the answers you can act on)

How can banks collaborate on AI fraud detection without sharing customer data?

Use privacy-preserving approaches: aggregated indicators, hashed identifiers, federated learning, and strict purpose limitation. Start with high-value signals (mules, scam destinations, device clusters) rather than personal details.

What’s the fastest way to improve cross-institution scam disruption?

Agree on a shared scam disruption workflow: rapid beneficiary risk scoring, shared escalation contacts, and standard response times for payment holds/recalls where permitted. Operational speed beats perfect detection.

Does collaboration increase compliance risk?

Not if it’s designed properly. Collaboration increases risk when it’s informal. Formal governance—scope, retention, access controls, audit logs, error correction—usually lowers risk because it reduces ad hoc sharing.

Where should fintechs start if they’re smaller than the banks?

Start by contributing what you can uniquely see: onboarding patterns, device intelligence, first-party behavioural signals, and fraud attempts that never reach the payment stage. Smaller players often detect new tactics earlier.

A practical 90-day plan for security collaboration with AI

If you want a plan that survives contact with the real world, run it like a delivery project.

1. Pick one shared use case (e.g., scam destination disruption on real-time payments).
2. Define the minimum viable signals (5–10 fields max: identifiers, timestamps, confidence, reason codes).
3. Set operational SLAs (who responds, in what timeframe, with what authority).
4. Pilot with two to three partners (a bank, a fintech, and a key vendor is a good mix).
5. Measure outcomes:
   • time-to-detect
   • time-to-action
   • prevented loss
   • false positive impact on customers
6. Only then expand the scope and participants.

This is also the moment to align AI governance: model monitoring, drift detection, and “human override” rules should be consistent enough that shared signals produce predictable responses.

Where this is heading in 2026: ecosystem defence, not company defence

The direction of travel is clear: financial security is becoming ecosystem defence. AI makes detection stronger, but it also accelerates attackers’ iteration loops. The institutions that win won’t be the ones with the fanciest model—they’ll be the ones that can coordinate, share signals safely, and act fast.

If you’re building or buying AI in finance—fraud detection, onboarding, credit scoring, or AI customer support—collaboration should be in the requirements from day one. Not as a “nice to have.” As part of the control set.

If you’re planning your 2026 roadmap now, ask this: Which two external partners would make your AI security twice as effective—and what would you need to share to get there?