Fraud Detection Needs a Consortium, Not a Solo Bank

Fraud teams have a dirty secret: even the best bank can only see a slice of the scam. The payment that looks “odd but plausible” in one institution often becomes obviously criminal when you can see what happened five minutes earlier at another bank, another fintech wallet, or a mule account at a third.

That’s why Mauriceo Castanheiro, Head of International Payments Fraud at Nasdaq Verafin, landed on a blunt point at Sibos 2025: modern scams can’t be solved at an individual institution level anymore. The criminals collaborate, share playbooks, and iterate fast. Banks and fintechs that fight fraud alone are basically trying to do incident response with one log file.

In this instalment of our AI in Finance and FinTech series (with an Australian lens), I want to unpack what “consortium power” actually means in 2025, where AI fits, and what practical steps fraud leaders can take without blowing up privacy, compliance, or operational reality.

Why fighting fraud alone fails in 2025

Answer first: Banks lose because fraud is now networked, and single-institution detection is inherently blind to cross-bank patterns.

International payments fraud, APP scams (authorised push payment), mule networks, synthetic identities, and account takeover don’t respect institutional boundaries. Criminal groups design attacks to look normal inside one bank while the broader chain is clearly abnormal across the ecosystem.

Three things changed in the last few years:

1. Scams shifted from “hack the bank” to “hack the customer.” Social engineering and impersonation tactics have become the main entry point, so traditional controls (passwords, device trust, even step-up authentication) can’t always stop a customer who’s been manipulated.
2. Fraud chains are multi-rail and multi-provider. Funds move through card, NPP/Osko-style instant payments, international wires, crypto on/off ramps, BNPL, and wallet transfers. That fragmentation creates hiding places.
3. Attack iteration is rapid. Fraud rings test variants daily. If each institution learns in isolation, the learning loop is too slow.

Here’s the uncomfortable truth: your “false positive” might be another bank’s confirmed scam. But if you can’t share intelligence fast enough, you’ll approve it anyway.

The hidden cost: good customers pay the price

When banks compensate by tightening rules, legitimate customers get blocked, delayed, or forced into clunky verification.

• Payments friction increases abandonment.
• Contact centres get swamped.
• Fraud analysts spend time clearing noise.

A consortium approach isn’t only about catching more criminals. It’s also about reducing collateral damage while keeping losses down.

What a fraud consortium actually is (and isn’t)

Answer first: A fraud consortium is a structured way for institutions to share signals, typologies, and risk intelligence so AI models can spot cross-entity patterns—without everyone dumping raw customer data into a giant pool.

People hear “consortium” and picture either:

• a magical central database that solves everything, or
• a compliance nightmare that legal will shut down.

The reality is more practical. Strong consortium programs focus on high-value, low-regret intelligence that travels well across members:

• Known mule account indicators (accounts repeatedly receiving small deposits then rapidly cashing out)
• Beneficiary risk (payee accounts that show scam-like behaviour across multiple originators)
• Device and session fingerprints (where permitted) that show coordinated behaviour
• Typologies (current scam scripts, impersonation patterns, emerging corridors)
• Entity resolution signals (confidence that two identities share attributes consistent with synthetic identity)

A good consortium is also governed:

• who can contribute what
• how data quality is validated
• how long signals persist
• how disputes and false accusations are handled

That governance matters because “share everything” is not a strategy—it’s a liability.

Consortium ≠ outsourcing your fraud program

I’ve seen teams hope a shared network will replace internal controls. It won’t.

Consortium intelligence is an amplifier. You still need:

• strong onboarding and KYC/AML controls
• transaction monitoring tuned to your products
• customer warnings and step-up friction where it actually works
• well-drilled scam response playbooks

The consortium gives you context your bank can’t create alone.

Where AI fits: from institution models to network intelligence

Answer first: AI is the engine that turns shared consortium signals into real-time decisions—by learning patterns across participants and scoring risk at the moment of payment.

Most fraud platforms already use machine learning for:

• anomaly detection
• behavioural biometrics
• graph analytics
• rules + model ensembles

What changes with consortium data is the signal richness.

Why graph-based AI is so effective for scam chains

Fraud is rarely a single event. It’s a sequence: compromised identity → new payee → first “test” transfer → mule hop → cash-out.

Graph analytics works because it models relationships:

• customer → device → login
• customer → payee → bank account
• bank account → other inbound/outbound transfers

With consortium participation, that graph becomes less parochial. A mule that looks “new” inside Bank A may be “seasoned” across the network.

A snippet-worthy way to say it:

Single-bank AI learns behaviour. Consortium AI learns behaviour plus reputation.

Reducing false positives with shared reputations

Fraud teams often trade loss prevention for customer experience. Consortium data helps break that trade-off.

Example scenario (common in instant payments):

• Your model flags a new payee as risky because it’s new, high velocity, or an unusual corridor.
• But consortium intelligence shows that payee has received thousands of legitimate salary-like deposits across multiple institutions with no scam markers.

Result: lower risk score, fewer unnecessary blocks.

AI guardrails: keep humans in the loop where it matters

Consortium + AI doesn’t mean “let the model decide everything.” The strongest setups use tiered decisioning:

• Auto-approve low-risk
• Step-up verification for medium-risk (customer confirmation, friction, warnings)
• Hold and review for high-risk

And critically: capture outcomes (confirmed scam, chargeback, law enforcement report, customer confirmation) so the network learns.

Designing consortium sharing without breaking privacy and compliance

Answer first: You can get consortium benefits while respecting privacy by sharing risk signals, hashed identifiers, and typologies rather than raw PII—and by using clear legal bases and governance.

In Australia, APRA expectations around operational risk, CPS 230-style resilience thinking, and the Privacy Act environment all push teams toward disciplined data sharing. The goal isn’t to share more. It’s to share smarter.

Practical models for safe collaboration

Common patterns that work in the real world:

1. Hashed or tokenised identifiers
   • Share irreversible hashes of account numbers, emails, phone numbers (with salts and governance) so members can match without exposing raw values.
2. Reputation scoring instead of raw details
   • “Payee risk score 92/100” plus reason codes is often enough to change a decision.
3. Federated learning / distributed model updates
   • Where feasible, models learn across participants without centralising training data.
4. Tiered access controls
   • Smaller fintechs might see different levels of detail than large banks, depending on regulation and data contribution.

The governance questions you should ask before joining

If you’re assessing a consortium approach (or building one), push for crisp answers:

• What problem are we solving first? (APP scams? mule detection? international wires?)
• What exactly is shared? (signals, reputations, typologies, graph edges)
• How do we prevent “poisoning” the network? (bad data, malicious flags, errors)
• How fast do signals propagate? (minutes matter in instant payments)
• How do we audit decisions? (model explainability, reason codes, dispute handling)

If you can’t get clear answers, you’re not looking at a consortium—you’re looking at a hope.

A practical playbook for banks and fintechs (next 90 days)

Answer first: Start with one scam type, integrate consortium signals into decisioning, measure lift, then expand—don’t attempt a “big bang” fraud transformation.

Fraud leaders are under pressure right now: end-of-year peak shopping, holiday travel, bonus season, and higher transaction volumes create easy cover for bad actors. If you’re planning for 2026 budgets, this is the window to build a plan with measurable outcomes.

Step 1: Pick one measurable use case

Choose a narrow lane where consortium intelligence clearly helps:

• APP scam prevention in instant payments
• mule account detection
• high-risk international corridors for SME payments

Define success metrics upfront:

• loss reduction (basis points of volume)
• recall rate / recovery rate improvement
• false positive reduction
• mean time to detect (MTTD) and mean time to respond (MTTR)

Step 2: Treat consortium signals like first-class features

Don’t bolt shared intelligence on as an afterthought. Feed it into:

• real-time scoring
• case management prioritisation
• customer intervention journeys (warnings, confirmation screens)

A small but powerful tactic: use consortium risk to decide when to add friction, not whether to do fraud control at all.

Step 3: Build a feedback loop that improves the network

Consortiums only get stronger when members contribute outcomes.

Operationally, that means:

• consistent disposition codes (confirmed scam vs customer error)
• rapid reporting of mule confirmations
• post-incident reviews that translate into updated typologies

Step 4: Prepare your “explainability story” for executives

When a payment is stopped, the business will ask why. You need a short, defensible explanation:

• behaviour signals (velocity, new payee, unusual device)
• consortium signals (payee reputation, mule network proximity)
• customer context (recent scam warnings ignored, prior compromise)

This is where AI + consortium can be stronger than rules: better reasons, not just more blocks.

People also ask: common consortium fraud questions

Do consortiums only help big banks?

No. Mid-tier banks and fintechs often benefit more because they get instant access to broader patterns they can’t observe with their own volume.

Will consortium sharing increase our regulatory risk?

Not if it’s designed well. The real risk is ad hoc sharing with unclear controls. A governed consortium with auditing, minimisation, and clear purposes is easier to defend.

Can AI replace scam education and customer warnings?

No. Scam prevention is behavioural. The best results come from AI-driven detection plus smart customer interventions that interrupt manipulation at the right moment.

What to do next if you want fewer losses and fewer false alarms

Consortium-level fraud intelligence is becoming table stakes because criminals already operate as a network. Castanheiro’s point from Sibos 2025 holds up in day-to-day operations: single-institution fraud detection is fighting with one hand tied behind its back.

If you’re building your 2026 fraud roadmap, I’d prioritise one thing: connect your AI fraud detection to consortium signals, then operationalise the feedback loop. That’s where the compounding gains show up—lower losses, faster detection, and fewer good customers caught in the net.

Where do you see the biggest gap today: cross-bank mule detection, APP scams, or international payments fraud corridors?