Fraud prevention has long been centered around the payment itself: detecting suspicious transactions, applying scoring engines, triggering step-up authentication, or blocking transfers at the final moment. But scams increasingly prove that this approach alone is no longer sufficient. By the time a payment instruction reaches a bank, the manipulation has often already happened: the victim has been convinced, pressured, coached, or emotionally pushed into authorizing the transaction themselves. In an era of instant and irrevocable payments, the time window for intervention at payment initiation is shrinking dramatically. That means scam prevention must move further upstream, towards the earlier moments where deception begins.
This is precisely the evolution I already described in my previous blogs "The First Line of Defense: Tackling Scams Before Transactions" (https://bankloch.blogspot.com/2025/09/the-first-line-of-defense-tackling.html") and "The Missing Link in Fraud Prevention: Real-Time Customer Dialogue" "https://bankloch.blogspot.com/2025/06/the-missing-link-in-fraud-prevention.html"), i.e. if scams start long before money moves, detection must start there too.
Recent initiatives clearly show that this shift has begun. Starling Bank recently introduced what it calls the UK’s first AI scam detection tool that allows customers to upload suspicious marketplace ads or screenshots before making a purchase decision. Instead of waiting for a fraudulent transfer to appear, the bank helps customers assess whether the commercial offer itself shows signs of deception. That changes the moment of intervention completely: fraud controls are no longer triggered by a payment, but by doubt. This is strategically important because the strongest fraud prevention often happens before intent to pay is fully formed.
The same logic appears in newer specialized players such as ScamGuardian, which uses AI-powered victim simulations to proactively understand scam techniques and generate actionable scam intelligence before attacks fully materialize. These approaches recognize a crucial reality: scams have become adaptive social-engineering systems, not merely suspicious transactions.
For consumers, however, early detection still starts with recognizing patterns. Many scams reveal themselves through signals that appear ordinary when viewed separately but become highly suspicious when combined. A fraudulent advertisement often comes from a weakly verifiable source: a domain that looks almost correct but contains small alterations such as "amaz0n" instead of "amazon", a shortened link hiding its destination, or branding that feels nearly, but not fully, authentic. Logos may be slightly distorted, fonts inconsistent, language unnatural, or contact details impossible to verify. Sometimes the offer claims to come from a celebrity with no credible connection to the product, or from a trusted brand using a recently registered domain. In many cases, a quick search combining the advertiser’s name with words like "scam" or "fraud" immediately reveals warnings from other victims.
The communication itself often provides equally strong clues. Generic greetings, poor grammar, mismatched sender addresses, unusual attachments, fake urgency, or emotional punctuation all remain highly reliable indicators. Scammers deliberately create pressure: limited availability, a countdown, an urgent family emergency, threats of account closure, or promises that "this opportunity disappears today." This urgency is critical to their success because it suppresses reflection.
The strongest scams increasingly combine several manipulation layers: they look legitimate, they create trust, and then they accelerate decision-making. Offers sound exceptionally attractive, e.g. extreme discounts, guaranteed investment returns, unusually large rewards, or rare opportunities. Payment instructions then often shift toward unusual channels: gift cards, crypto-assets, transfers to third parties, money transfer apps, or urgent wire transfers outside normal procedures.
Another important warning sign remains secrecy. Any request to keep an interaction confidential, bypass official channels, avoid consulting the bank, or move conversations to private messaging platforms such as WhatsApp or Telegram should immediately trigger suspicion. Legitimate institutions do not ask customers to hide interactions from their own bank, employer, or relatives.
But the burden cannot remain solely with consumers. The complexity of modern scams means every stakeholder in the chain must intervene earlier.
Banks are beginning to test this broader role. Revolut recently launched Street Mode, a feature designed to address an emerging scam scenario: transfer mugging after phone theft. Customers can define trusted locations, and outside these zones additional checks and time delays are introduced for outgoing transfers. This is a powerful example of contextual fraud prevention: location, behavioral risk, and timing are combined before money irreversibly leaves the account.
Similarly, KBC Group introduced "Engelbewaarder" ("Guardian Angel"), allowing customers to appoint a trusted person who receives an alert when suspicious payments are detected. This introduces an external human validation layer exactly where social engineering is strongest: when victims themselves are manipulated into authorizing fraud. Especially in advanced human takeover scenarios, this kind of shared decision model can be highly effective.
Governments also increasingly recognize that scam prevention must happen before victim contact scales. In Belgium, the Belgian Anti-Phishing Shield (BAPS), coordinated by Centre for Cybersecurity Belgium, blocks malicious domains directly at DNS level. This means users are redirected before they even reach fraudulent websites. The integration of the PhishNemo project, developed by the Federal Judicial Police, adds an even earlier layer by detecting suspicious domains before phishing campaigns are broadly launched. Instead of waiting for complaints, suspicious infrastructure is identified and neutralized upstream.
That model matters because phishing increasingly industrializes infrastructure: domains are registered in bulk, cloned rapidly, and activated only briefly. Detecting domain anomalies, monitoring naming patterns, and blocking suspicious registrations before they become active can reduce large parts of the attack surface.
Public awareness remains another critical layer. Belgian campaigns such as #SCAM (“Stay Connected, Act Mindfully”), Safeonweb reporting channels, and *anti-phishing collaboration between banks and telecom operators_ show that prevention increasingly requires ecosystem coordination.
The strategic lesson is clear: scams can no longer be treated as isolated payment fraud events. They are journeys, starting with exposure, continuing through trust-building, pressure creation, identity manipulation, and only ending in payment execution. If controls remain concentrated only at the transaction itself, they intervene too late.
As payments become instant, the old reactive model loses effectiveness. Fraud prevention must therefore become distributed across the full scam journey: suspicious domains blocked before victims click, deceptive ads analyzed before customers trust them, suspicious conversations detected while persuasion happens, contextual warnings triggered during decision-making, and payment controls still acting as final safeguard.
The future of scam prevention is therefore not stronger blocking alone. It is earlier intelligence, broader cooperation, and better timing, because the most effective scam prevention happens before a payment ever exists.
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