Fraud by customers and third parties is a fact of life for every business. But what’s the best way to respond?
The status quo approach is to detect first, then respond. Spot a fraudulent transaction after it happens and then stop any fallout or recoup the loss as rapidly as possible.
Unfortunately, the fraud detection systems that fintechs, banks, and others use to perform this critical action were built for yesterday’s fraud.
They’re good at spotting anomalies like a login from an unusual IP or transfer over a specific limit, but they weren’t designed for banking deepfake fraud onboarding scams, AI-powered phishing kits, or synthetic identity creation using stolen data from previous breaches.
Worse still, these systems are actually relatively easy for attackers to bypass. The result is a kind of cat-and-mouse game between fraud teams and fraudsters. New fraud methods are developed, new detection systems rolled out, new bypasses discovered by fraudsters, and so on….
Attackers reverse-engineer risk models, test variations, and learn how to avoid detection.
Meanwhile, legitimate users get flagged, delayed, or locked out. That frustration leads to abandoned transactions, dropped accounts, and support tickets that erode brand trust.
In high-growth environments, such as a scale-up fintech onboarding new users, detection-centric systems can be especially punishing. False positives at the KYC stage not only increase friction but also risk driving away good customers before they’ve even started using the product.
What Financial Fraud Prevention Really Means
Banking fraud prevention evolves the status quo fraud posture.
Instead of assessing how “risky” an action looks, it verifies whether the action is being taken by the legitimate person and does so across the entire session, not just at login.
For example, biometric fraud prevention tools like IronVest combine:
- Liveness detection to confirm the user is physically present, not spoofed.
- Behavioural analytics (e.g., typing patterns, mouse movements) to continuously verify identity.
- Session-level monitoring to ensure every action (e.g., transfers, changes to account info, and logouts) is made by the verified user.
This approach reduces fraud and improves the customer experience by removing unnecessary verification steps. Authentication occurs invisibly, in the background, without compromising usability.
Fraud Detection vs. Prevention
If your business has a fraud issue, there are two kinds of approaches you can take to fight it:
- Fraud detection is reactive. It relies on risk scores, static rules, and post-event analysis. It attempts to detect fraud after it has begun. Think traditional banking fraud detection tools like Mastercard’s Decision Intelligence suite.
- Fraud prevention, on the other hand, is proactive. It’s built to stop fraudulent activity before any harm is done. Instead of guessing who might be a risk, it verifies continuously that the person taking action is legitimate. An example of a fraud prevention solution is IronVest Authentic Action technology.
Lessons from Fintechs and Challenger Banks
The difference between fraud detection and protection is most apparent in fast-moving fintechs.
Early-stage fintechs often default to lightweight fraud tools, such as basic device checks, email validation, and off-the-shelf risk-scoring APIs.
These are sufficient at a low scale but often break down under growth.
Revolut, for example, faced criticism for freezing legitimate accounts due to false positives during rapid expansion.
More mature platforms, such as Plaid and Stripe, have begun to integrate continuous user verification, particularly for high-risk services like account linking, payouts, or financial data sharing.
Others, such as Feedzai and ComplyAdvantage, focus on behavioural analysis and collaborative intelligence to predict and prevent fraud in real time before users ever see an alert.
Key takeaway: Companies that adopt a preventive approach early tend to scale more smoothly, reduce support costs, and retain a higher percentage of high-value users.
Prevention Supports Compliance and Efficiency
There’s a common misconception that prevention is a “nice to have” – something that comes after core detection is in place.
In reality, prevention helps meet compliance obligations and reduces long-term costs.
In the EU, for example:
- PSD2 mandates strong customer authentication, but most banks still only enforce this at login or transaction points.
- The upcoming PSD3 framework and the AI Act both emphasise the importance of auditability and transparency in real-time decision-making.
- Deterministic identity verification, a hallmark of protection systems, produces auditable logs that meet these evolving standards far better than probabilistic detection models.
Operationally, protection helps reduce fraud alert volumes, false positives, and manual investigations.
Fraud teams can spend less time triaging ambiguous signals and more time improving models and workflows. Compliance teams benefit from better logs. Customer support sees fewer complaints.
In other words, by shifting from detection to prevention, businesses not only stay ahead of evolving fraud tactics but also gain a competitive advantage.
