Canada’s Anti-Fraud Agency Puts Banks on the Hook

Online fraud isn’t “someone else’s problem” anymore. When a government announces a dedicated financial crimes agency—like Canada is doing right now—it’s a signal that scams have become frequent, scalable, and coordinated enough to demand a national response.

The part that should grab every bank and FinTech leader is this: Canada’s national anti-fraud strategy is expected to require more action from banks. That’s not bureaucratic noise. It’s a shift in accountability. Regulators can stand up new agencies, but the fraud actually happens where money moves—inside payment rails, digital onboarding flows, call centers, and mobile apps.

For readers following our AI in Finance and FinTech series, this is the moment where policy meets practice. A new financial crime agency can coordinate intelligence and enforcement. But AI-powered fraud detection is what changes day-to-day outcomes: fewer authorized push payment scams, fewer mule accounts, fewer synthetic identities, and fewer customers blaming you (fairly or not) for letting it happen.

Why Canada is building a financial crime agency now

Canada’s decision to set up a financial crimes agency is a direct response to an ugly trend: online scams scale faster than traditional policing can adapt. Criminal groups don’t need to physically rob a bank when they can run phishing kits, romance scams, “safe account” impersonations, and business email compromise campaigns from anywhere.

The fraud wave is digital, but the damage is very real

Modern scams combine three forces:

• Social engineering that works (impersonation, urgency, trust-building)
• Instant payments and faster settlement (less time to intervene)
• Data leakage and identity fragments (enough to pass weak checks)

A national financial crimes agency helps because it can centralize reporting, coordinate cross-jurisdiction investigations, and standardize how financial institutions share fraud typologies. That’s the boring phrase that actually matters: typologies are patterns. Pattern-sharing is how you stop the same scam from hitting 50 institutions in a week.

A big policy message: banks will be expected to do more

When a strategy “requires more action from banks,” it typically translates into some mix of:

• Stronger fraud controls (especially for high-risk transfers)
• Better customer protection and complaint handling
• More rigorous AML and mule account detection
• Faster information sharing with law enforcement and peers
• Clearer liability expectations when preventable fraud occurs

My take: this direction is overdue. Fraud losses aren’t just numbers; they’re customer trust, brand risk, and operational drag.

What “more action from banks” looks like in practice

“Do more” can’t mean “add more manual reviews.” Manual review doesn’t scale, and it burns out teams while still missing fast-moving scams. What regulators and customers actually need is measurable control improvements.

1) Real-time scam detection (not just transaction monitoring)

Traditional transaction monitoring is often tuned for known fraud patterns and compliance thresholds. Scam prevention needs something different: intent detection.

That means looking at signals like:

• New payee + first-time payment + unusual amount
• Device changes, SIM swaps, remote access tool fingerprints
• Sudden behavior shifts (login time, geolocation variance, payee velocity)
• High-risk beneficiary banks or accounts that appear in recent scam clusters

This is where machine learning fraud detection earns its keep: it can score risk in milliseconds using hundreds of features, not just a few rules.

2) Mule account prevention: onboarding and lifecycle controls

Most scam money needs a place to land. Mule accounts—opened with stolen IDs, synthetic identities, or recruited “money mules”—are the plumbing.

Banks that reduce mule capacity usually do three things well:

• AI-driven identity verification at onboarding (document + selfie + liveness + device reputation)
• Early-life monitoring (first 30–90 days is often the highest risk)
• Network analytics to detect rings (many accounts funneling to a small set of endpoints)

If Canada’s new agency improves intelligence sharing, mule patterns will be easier to spot across institutions. But each institution still has to act on that intelligence.

3) Stronger “step-up” friction that doesn’t ruin UX

Fraud teams hate friction because it can cost conversions. Product teams hate friction because it can cost growth. The compromise is adaptive friction—only adding steps when the risk score is high.

Examples that work:

• Confirming payee name-match or warning on mismatches
• Cooling-off periods for first-time high-value payees
• Out-of-band confirmations for unusually risky transfers
• Clear “this is a scam pattern” messaging (not vague warnings)

A simple stance I’ve found helpful: friction is either targeted or pointless. AI helps you target it.

Where AI fits: the modern fraud stack for banks and FinTechs

AI isn’t a single model you buy and switch on. In high-performing fraud programs, it’s a stack: models, data pipelines, decisioning, human review, and feedback loops.

AI-powered fraud detection: what it should actually do

A useful AI fraud system should:

1. Score risk in real time (before funds leave)
2. Explain the decision enough for analysts and audit (top drivers, reason codes)
3. Learn quickly from confirmed fraud and false positives
4. Resist adversarial behavior (attackers probing thresholds)
5. Support investigations (case clustering, entity resolution)

A common misconception is that generative AI replaces fraud analytics. It doesn’t. Generative AI is best as a copilot—summarizing cases, drafting SAR narratives, and surfacing similar incidents—while predictive models do the scoring.

Data signals that matter more than people think

Banks already have most of what they need, but it’s often siloed. High-signal inputs include:

• Device ID, OS version, emulator/root indicators
• Behavioral biometrics (typing cadence, touch pressure, navigation patterns)
• Payee creation metadata and payee reuse patterns
• Graph signals (shared devices, shared beneficiaries, shared contact points)
• Contact center signals (voice stress markers, scripted phrases, callback patterns)

When Canada increases expectations on banks, the winners won’t be the ones with “more data.” They’ll be the ones with integrated data.

Lessons from Australia: what banks and FinTechs can borrow

Our AI in Finance and FinTech series often comes back to Australia for a reason: Australian banks and FinTechs have been under sustained pressure from scams, real-time payments, and sophisticated social engineering.

Here are three practical lessons that translate well to Canada’s push.

1) Treat scams as a product problem, not only a fraud problem

If the scam happens through your app, your app should help stop it.

That means product patterns like:

• Risk-aware payment flows
• Clear payee verification cues
• In-app scam education triggered by behavior (not generic pop-ups)

When scams are treated as “fraud’s problem,” product teams ship features that expand attack surface. When it’s treated as “everyone’s KPI,” scam losses drop.

2) Focus on authorized push payment (APP) fraud workflows

APP scams are brutal because the customer “authorizes” the transfer, even though it’s manipulated. The operational fix is a tight loop:

• Detect risk before sending
• Intervene with clear, specific warnings
• Provide fast recall attempts when funds move
• Investigate mule networks and block downstream routing

This is exactly where governments and banks can complement each other: agencies can pursue takedowns; banks can stop the flow.

3) Benchmark using outcomes, not activity

Counting “alerts reviewed” is activity. Counting “scams prevented” is an outcome.

Better metrics:

• Scam loss rate per 10,000 payments
• False positive rate at key thresholds
• Time-to-intervention (from trigger to block/warn)
• Mule account detection within first 30 days
• Recovery rate (how much is recalled/returned)

If Canada’s new agency adds reporting expectations, outcome metrics will matter even more.

A practical playbook: how to respond in the next 90 days

Banks and FinTechs don’t need a three-year transformation to show progress. A 90-day plan can materially reduce losses.

Step 1: Map your top 5 scam journeys end-to-end

Write them like user stories, including where customers are manipulated:

• Impersonation (“bank security team”)
• Crypto investment and “recovery” scams
• Invoice redirection (SME-targeted)
• Romance scams
• Marketplace scams

Then attach controls to each stage: onboarding, payee creation, pre-send checks, post-send recall.

Step 2: Add adaptive friction to the riskiest moments

Start with the highest ROI control points:

• First-time payees
• High-value transfers
• Device changes + payee creation + payment within one session

This is where AI risk scoring plus good UX messaging stops real scams.

Step 3: Upgrade mule detection with graph + entity resolution

Mule networks don’t show up as “one bad account.” They show up as connections.

If you only do per-account rules, you’ll stay behind. Add:

• Entity resolution across devices, emails, phone numbers, IPs
• Graph clustering to detect rings
• Shared beneficiary and shared device detection

Step 4: Give analysts an AI copilot (carefully)

Used well, generative AI reduces case handling time by:

• Summarizing account activity into a timeline n- Drafting investigation notes and escalation summaries
• Suggesting similar past cases based on typology

Guardrails matter: don’t let a model “decide” blocks. Let it accelerate human decisions.

A good rule: predictive models score risk; generative models explain context.

What this means for 2026: coordinated enforcement + automated prevention

Canada building a financial crimes agency is a strong move, but it won’t stop scams on its own. The real win is when enforcement coordination meets bank-side automation.

Banks and FinTechs that invest now in AI-powered anti-fraud strategies will be better positioned for whatever “more action” becomes—new reporting requirements, tighter liability frameworks, or stricter expectations around scam reimbursement.

For teams following our AI in Finance and FinTech series, this is also a strategic moment: fraud prevention isn’t just defense. It’s a retention strategy. It’s a cost strategy. And it’s quickly becoming a regulatory strategy.

If you run fraud, risk, compliance, or payments: which part of your customer journey still assumes “the customer will figure it out”? That’s probably where your next scam loss will come from.