AI Scam Monitoring in Banking: What Starling Signals

Scams aren’t just “fraud problems” anymore—they’re product problems. If customers lose money (or even just confidence) while using your app, they’ll move. That’s why Starling’s move to launch an AI tool for scam monitoring is more than a feature release. It’s a signal that real-time, AI-driven protection is becoming table stakes for digital banking.

I’ve found the banks and fintechs that win on trust do two things well: they spot risky behaviour early and they intervene in a way that helps customers without blocking legitimate payments. That’s the hard part. Anyone can throw a rules engine at fraud. The leaders build monitoring that adapts as scammers change tactics—daily.

This post sits within our AI in Finance and FinTech series, and it’s aimed at teams in Australian banks and fintechs who are trying to reduce scam losses, improve customer outcomes, and turn security into a measurable advantage.

Why AI scam monitoring is replacing “fraud rules”

AI scam monitoring is taking over because scams now look like legitimate customer behaviour—right up until the moment money leaves.

Traditional fraud detection was built for stolen cards, account takeovers, and obvious anomalies. Scam payments are different. The customer often initiates the transfer, passes 2FA, and may even reassure the bank it’s fine because they’re being coached in real time by the scammer.

That’s the gap Starling is addressing with an AI-driven approach: monitoring patterns that suggest coercion, social engineering, and manipulation, not just unauthorised access.

Scams are operationally “fast,” rules are operationally “slow”

Rules-based controls tend to follow a cycle:

1. Analysts see a new scam pattern.
2. Someone designs a rule.
3. It’s tested, tuned, approved.
4. It ships—often days or weeks later.

Scammers don’t wait.

A well-designed AI monitoring layer can shorten that cycle by learning from emerging patterns (with human review) and scoring risk in real time—especially when combined with customer-level context like historical payees, transaction cadence, device signals, and behavioural biometrics.

Real-time monitoring is the difference between “flagged” and “prevented”

Banks already investigate fraud after the fact. The higher value move is stopping the payment or slowing it down before funds hit mule accounts.

Real-time AI scam monitoring supports actions like:

• In-app friction (stronger warnings, confirm screens that change wording)
• Step-up verification (extra authentication only when risk is high)
• Payment holds (short delays for high-risk transfers)
• Outbound call-backs routed to specialist scam teams

Customer experience matters here. If you block too much, customers hate you. If you block too little, customers don’t trust you. AI is often the only practical way to balance those trade-offs at scale.

What “AI tool for scam monitoring” usually means (and what it should mean)

An AI scam monitoring tool should combine multiple models and signals, not a single magic algorithm.

Because we don’t have full details from the source page (the publisher returned an access restriction), we’ll focus on what this category of tool typically includes—and what you should look for if you’re an Australian bank, neobank, or fintech evaluating similar capabilities.

Behavioural signals: the best early warning system

Scam victims often behave differently under pressure:

• Sudden urgency: larger transfers, faster repetition
• Unusual payee creation followed by immediate transfer
• Increased failed attempts (mistyped details, multiple amount edits)
• Device/app behaviour shifts (rapid screen switching, copy/paste patterns)

A strong AI monitoring system doesn’t treat these as proof. It treats them as risk indicators that are more predictive when they cluster.

Network intelligence: follow the money, not just the user

Scam prevention improves dramatically when you evaluate:

• Payee risk (new payee, prior reports, mule-like behaviour)
• Destination account velocity (many incoming transfers from unrelated senders)
• Funnel accounts and rapid cash-out patterns

This is where AI in banking becomes a team sport: the model is only as good as the feedback loop from investigations, chargeback/complaints data, and external intelligence.

Contextual customer modeling: treat “normal” as personal

“Normal” isn’t the same for a uni student, a gig worker, and an SME owner.

Customer-level baselines typically include:

• Typical transaction size range
• Typical payment counterparties
• Normal activity times and frequency
• Geolocation/device consistency

The practical win: fewer false positives because you’re comparing the customer to themselves, not to an average.

A good scam monitoring system doesn’t try to be perfect. It tries to be usefully early.

Why this matters for Australian banks and fintechs right now

Australian financial institutions are under intense pressure to reduce scam losses, improve customer outcomes, and demonstrate strong controls.

Even without naming specific regulatory updates, the direction is clear: accountability is rising, reporting expectations are rising, and customer tolerance for “sorry, nothing we can do” is close to zero.

Customer trust is becoming a measurable growth lever

If you’re running acquisition campaigns, scam stories undermine everything:

• Higher abandonment during onboarding
• Lower funding rates after first deposit
• Reduced usage of instant payments
• More calls to contact centres (higher cost-to-serve)

Scam monitoring isn’t just a cost centre; it’s a conversion and retention lever—especially for digital-first brands.

Real-time payments raise the bar

Instant payment rails mean the time between “authorised” and “gone” is shrinking.

That pushes banks toward predictive controls rather than detective controls:

• Predictive: stop or slow risky transactions
• Detective: investigate after money has moved

If your fraud stack is still mostly detective, you’ll keep losing the race.

Designing AI scam monitoring that customers actually accept

AI can detect risk, but the customer interaction determines whether prevention works.

A common mistake is treating warnings as legal disclaimers. Victims under coercion click through those in seconds. Interventions need to change behaviour.

Use “friction” like a scalpel, not a hammer

The best interventions are targeted and situational:

• If the customer is adding a new payee and sending a large first payment, show a specific message: “This looks like a common scam pattern.”
• If it’s a crypto exchange transfer, prompt for purpose selection and show scam indicators relevant to investment scams.
• If the model sees repeated high-risk attempts, move from prompts to step-up checks.

Subtle but important: rotating message wording reduces “banner blindness,” and behavioural science beats generic warning screens.

Give customers a safe “escape hatch”

Scammers often stay on the phone and instruct victims what to do. Your UX should help customers break that spell:

• Offer a one-tap “I’m being pressured” option
• Encourage a pause: “Take 5 minutes before sending”
• Provide a fast route to a trained specialist (chat or callback)

These actions should be triggered by AI risk signals, not buried in settings.

Make explainability operational, not academic

No one needs a 10-page model explanation in the app. But internally, teams need:

• Clear reason codes (new payee + unusual amount + known mule cluster)
• Audit trails for decisions
• A/B testing frameworks to evaluate interventions

If you can’t explain it to your fraud ops team, you can’t improve it.

A practical blueprint for implementing AI scam monitoring

If you’re a product leader, risk leader, or CTO, here’s an approach that works in the real world.

1) Start with a “scam taxonomy” you can measure

Define categories you care about, for example:

• Payment redirection (invoice and supplier scams)
• Romance and impersonation scams
• Investment/crypto scams
• Remote access and “bank security team” scams

Then define what success looks like:

• Reduction in scam loss rate per 10,000 customers
• Increased prevented loss value
• False positive rate (blocked good payments)
• Time-to-intervention

2) Build a feedback loop that doesn’t depend on luck

Models die without feedback.

Set up pipelines for:

• Confirmed scam reports (customer + investigation)
• Complaint reasons and outcomes
• “Near miss” events (warnings shown, user abandoned payment)
• Recovery outcomes (funds returned vs unrecovered)

This turns scam monitoring into a learning system rather than a static control.

3) Combine model outputs with decision policy

AI should recommend risk; policy decides action.

A typical decision policy looks like this:

• Low risk: allow
• Medium risk: warn + confirm purpose
• High risk: step-up auth + stronger warning
• Very high risk: hold payment + specialist review

This is where you tune customer experience.

4) Treat privacy and security as design constraints, not paperwork

Australian customers care about privacy, and regulators care about governance.

Operationally, that means:

• Minimising the data you collect
• Protecting model features and outputs like sensitive data
• Separating duties (builders vs approvers)
• Monitoring model drift (scammers force drift)

You can move fast without being reckless, but you need the discipline.

“People also ask” answers (the ones your stakeholders will raise)

Does AI scam monitoring reduce losses on authorised payments?

Yes—when it’s paired with intervention design. Detection without intervention is just a dashboard.

Will AI increase false positives and frustrate customers?

It can, if you treat all risk the same. The goal is tiered intervention: minimal friction for most payments, heavier checks only when signals cluster.

Is this only for big banks with huge data teams?

No. Fintechs can start with focused use cases (new-payee transfers, high-risk merchant categories, mule detection) and grow coverage. The bigger constraint is usually governance and feedback loops, not model complexity.

How do you measure success beyond “losses went down”?

Track:

• Prevented loss value
• Conversion impact (drop-off after warnings)
• Contact centre volume for scam issues
• Repeat victimisation rate
• Investigation workload per 1,000 customers

If investigation workload spikes, your model or policy is too aggressive.

Where Starling’s move points next

Starling launching an AI tool for scam monitoring fits a broader trend: fraud detection is shifting from static controls to adaptive, real-time systems. Australian banks and fintechs are on the same track for one simple reason—instant payments and sophisticated social engineering force it.

If you’re building in this space, my strong view is this: don’t treat scam monitoring as a side project under “security.” Treat it as a core product capability. The teams that do will see lower losses, fewer support escalations, and stronger customer trust.

If you’re assessing your roadmap for 2026, here’s the question that cuts through the noise: When a customer is being manipulated in real time, what does your app do—right now—to stop the money leaving?