AI Fraud Detection: Smarter Defenses for Fintech

Fraud has learned a nasty trick: it looks more like your real customers every quarter. The shift isn’t subtle. Criminals are using richer data, faster infrastructure, and better “playbooks” to copy legitimate behaviour—right down to device fingerprints, browsing patterns, and payment habits.

That’s why data-driven fraud is such a headache for Australian banks and fintechs right now. When the fraud is data-driven, the defence has to be data-driven too—meaning AI-powered fraud detection that works in real time, adapts quickly, and doesn’t crush conversion with false declines.

This post is part of our AI in Finance and FinTech series, and I’m going to take a stance: most fraud programs are still designed for last decade’s threats. If you’re relying on static rules and yesterday’s alerts, you’re handing attackers the map.

Data-driven fraud is winning because controls are predictable

Fraud scales when your controls are easy to learn. If an attacker can test small transactions, observe outcomes, and iterate, your “defences” become a feedback loop that trains them.

The common pattern:

• Rules get published indirectly through outcomes (“Transactions above $X get blocked”, “First-time payees always trigger step-up”).
• Attackers probe at low cost, using mule accounts and disposable devices.
• They move to higher value once they understand your thresholds and processes.

Static controls can still catch obvious abuse, but they struggle against modern tactics like authorised push payment (APP) scams, account takeover (ATO), and synthetic identity fraud—where the activity can look legitimate until it’s too late.

The myth: “More rules” equals better fraud prevention

Most companies get this wrong. Adding rules often increases friction for good customers while fraudsters route around them.

Rules are useful for:

• Compliance-driven hard blocks (e.g., sanctioned entities)
• Known bad indicators with low false positives
• Simple guardrails for new products

Rules are not enough for:

• Evolving scam scripts
• Coordinated attacks spread across accounts
• Behavioural mimicry (fraud that “acts normal”)

When fraud adapts daily, defence can’t update weekly.

What “smarter solutions” actually means: AI that detects intent

“Smarter solutions” isn’t a vague promise. In practice, it means you’re modelling behaviour, relationships, and anomalies at machine speed—then deciding the least invasive control that still reduces loss.

At a high level, AI improves fraud prevention in three concrete ways:

1. It spots patterns humans don’t see (subtle combinations of signals across device, network, account, and payment flows).
2. It adapts faster than rules (models retrain as attackers shift tactics).
3. It optimises outcomes, not just blocks (reducing false positives and protecting conversion).

Signals that matter in AI-driven fraud detection

If you want AI to work, feed it the right signals. The goal is to understand: Is this customer in control? Is this session consistent? Is this payment part of a scam pattern?

High-value signal categories include:

• Device & session intelligence: emulator detection, device changes, sensor anomalies, browser integrity
• Behavioural biometrics: typing cadence, swipe pressure, navigation loops, hesitation points
• Network signals: proxy/VPN risk, IP reputation, ASN anomalies, geo-velocity
• Payment graph signals: payee novelty, network of beneficiary accounts, hop patterns
• Customer history context: typical transaction sizes, time-of-day habits, channel usage

A practical benchmark I use: if your fraud decisioning is based on fewer than ~20 meaningful signals, you’re probably over-indexing on blunt controls.

Real-time fraud monitoring: decisioning beats detection

Catching fraud is nice. Stopping it in the moment is what changes the loss curve.

Real-time fraud monitoring needs two things that many stacks don’t have yet:

• Low-latency feature computation (you can’t wait seconds for a decision in a checkout flow)
• Action orchestration (what happens after you flag risk matters as much as the flag)

A modern decision ladder (reduce friction, keep safety)

Instead of “approve/decline,” better programs run a ladder of actions. Here’s a simple version that works well in banking and fintech:

1. Approve silently (low risk)
2. Step-up authentication (medium risk): passkeys, biometrics, in-app confirmation
3. Out-of-band confirmation (higher risk): trusted device prompt, verified call-back
4. Hold and review (very high risk): queue with context, not just an alert
5. Decline/block (critical risk): with clear customer messaging and recovery path

The trick is pairing model confidence with customer experience. If your model isn’t strong enough to support silent approvals, you’ll default to friction and lose good users.

Snippet-worthy truth: “Fraud prevention is a customer experience problem with a loss line attached.”

The hard part: scams and authorised payments

In Australia, scam losses remain a board-level issue. And scams are tough because the customer often authorises the transaction.

Traditional fraud systems are built for unauthorised activity: stolen credentials, stolen cards, suspicious merchant patterns. Scam prevention needs something different: detecting coercion, manipulation, and abnormal decision context.

What AI can do for scam detection (that rules can’t)

AI models can learn scam-like sequences that look normal in isolation:

• Sudden creation of a new payee + immediate high-value transfer
• Multiple failed payee additions followed by success
• Login from a known device but abnormal navigation (help pages, limits, password screens)
• Rapid limit changes followed by transfers
• Unusual combinations of channels (web login + mobile transfer + call centre contact)

Banks and fintechs that do this well don’t just block payments; they interrupt the scam script with targeted friction:

• “This payee has been reported by other customers” style warnings (careful with wording)
• Cooling-off periods for first-time high-value payees
• In-app confirmation that restates the risk in plain language
• Fast access to human support when the model detects scam likelihood

I’m opinionated here: if your only scam control is “add more warnings,” you’re not serious. Scammers train customers to ignore warnings. Your controls have to change the flow.

Building an AI fraud detection program that actually works

Buying a model isn’t a strategy. The best outcomes come from treating fraud as a data product with clear ownership, measurement, and iteration.

Step 1: Define the outcomes (not just the alerts)

Pick metrics that reflect reality:

• Fraud loss rate (basis points of volume)
• False positive rate (good transactions challenged/declined)
• Challenge pass rate (how often step-up works)
• Time-to-detect and time-to-contain (minutes, not days)
• Scam interruption rate (payments prevented or recovered)

If you can’t measure it, your model will drift and nobody will notice until losses spike.

Step 2: Fix the data plumbing before you scale

AI needs consistent, timely data. Common blockers in financial services:

• Siloed channel data (mobile vs web vs call centre)
• Limited device telemetry due to legacy front ends
• Event schemas that change without versioning
• Feature calculations done ad hoc in notebooks

The answer is boring but effective: a stable event pipeline, a shared feature store (or feature management layer), and strict monitoring for latency and completeness.

Step 3: Combine model types (one model won’t cover everything)

Fraud is multi-modal. Use a portfolio:

• Supervised models for known fraud labels (ATO, card-not-present, etc.)
• Anomaly detection for new attack patterns
• Graph machine learning for mule networks and beneficiary relationships
• Natural language processing for contact centre notes and scam narratives (when policy allows)

This is where “AI in finance” becomes practical: different models map to different fraud problems.

Step 4: Put humans where they add value

Analysts shouldn’t be triaging low-quality alerts. AI should prioritise cases with context:

• Why the risk score is high (top contributing signals)
• What changed vs the customer baseline
• Whether the beneficiary/account is connected to known risky clusters
• Suggested action and expected customer impact

Treat explainability as an operations tool, not just a compliance checkbox.

“People also ask” (quick answers)

Can small fintechs use AI for fraud detection without a huge team?

Yes—if you start with a narrow use case (like ATO or first-time payee risk) and instrument your product properly. The biggest constraint is usually event quality, not headcount.

Does AI increase false positives?

Bad implementations do. Strong implementations reduce false positives because models use richer context than rigid thresholds. The goal is fewer blanket rules and more targeted step-ups.

What’s the fastest win for Australian banks tackling scams?

Improve first-time payee controls with behavioural context and real-time risk scoring, then add a playbook that interrupts high-likelihood scam flows (cooling-off, confirmations, and rapid support).

Where this is heading in 2026: identity, networks, and stronger authentication

Looking into 2026, fraud programs will split into two lanes:

• Identity assurance (proving the right person is present): passkeys, device binding, continuous authentication
• Network intelligence (finding coordinated fraud): graph analysis across beneficiaries, mule rings, and device clusters

AI sits in the middle, translating messy signals into crisp decisions. And the teams that win won’t be the ones with the most dashboards—they’ll be the ones with the fastest learning loop.

Fraud is already data-driven. The remaining question for banks and fintechs is whether your defence is equally data-driven—and whether your AI fraud detection can act in real time without punishing genuine customers.

If you’re reviewing your 2026 roadmap right now, start here: pick one high-loss journey (ATO, first-time payee scams, card-not-present), instrument it end-to-end, and build a decision ladder that treats customer experience as part of security. What would you change first if you had to cut losses by 20% in the next two quarters?