Transaction review
◇ Hold before release- Beneficiary
- New payee
- Amount
- €8,950
- Device
- Trusted
- Behaviour
- Unusual urgency
Behaviour risk score78/100
Verification requiredbefore payment approval
Book a pilot
INTELLIGENT PAYMENT PROTECTION
The scam succeeds when the payment goes through.
Banks are good at verifying who is making a payment. The harder challenge is knowing whether a genuine customer is being manipulated into making it. VERIF-AI detects behavioural risk before funds are released, adding an extra layer of protection at the moment it matters most.
- Transaction-level analysis
- Behaviour-based risk scoring
- Human review for high-risk cases
01 / THE PROBLEM
The customer approves the payment.
The scammer controls the decision.
Scammers bypass security by exploiting the customer, not the bank. A genuine customer authenticates, adds the payee and approves the transfer, making the transaction appear legitimate until it is too late to recover the funds.
Payment fraud reported across the EEA
EBA and ECB reporting demonstrates the scale of payment fraud across Europe, reinforcing the need for prevention before funds are released.
EBA / ECB sourceWHY PHISHING KEEPS WORKING
The attacker doesn’t need to break the bank.
They break the customer
Modern fraud bypasses secure systems, MFA and login protections by manipulating real people. By the time fraud is detected, the money is already gone.
Impersonation creates trust
Attackers pose as a bank, police, supplier or executive and create a believable reason to move money.
Urgency bypasses judgement
Pressure, fear and time limits push customers to act before they verify what is happening.
Authentication looks legitimate
The user is genuine, the device may be genuine and the login may be genuine—but the intent has been manipulated.
The loss happens at payment
Once the transaction is executed, the bank is left with recovery, reimbursement and customer-impact costs.
“Banks don’t lose money because phishing exists. Banks lose money because fraudulent transactions are still executed.
THE MISSING LAYER
Banks secure access.
VERIF-AI secures the decision to pay
MFA, awareness training, secure apps, email protection and fraud monitoring are essential. But when a genuine customer is being manipulated, every traditional control can still pass. VERIF-AI adds a decision layer at the point of payment.
- ✓ MFA
- ✓ Awareness
- ✓ Secure apps
- ✓ Email protection
- ✓ Fraud monitoring
- 01
Authentication
Identity and access checks succeed.
- 02
Payment decision
A genuine customer initiates and approves the transfer.
- 03
Money gone
The manipulation is discovered after the funds have moved.
✓Intervene before release
VERIF-AI acts where traditional security stops.
We analyse transaction context and customer behaviour before release—while there is still time to intervene.
HOW VERIF-AI WORKS
A payment can look legitimate.
The behaviour behind it may not be.
This illustrative transaction shows how VERIF-AI evaluates payment context and customer behaviour to identify signs of manipulation before funds are released.
Illustrative transfer #VX-29481
● Transaction analysis- Amount
- €12,400
- Beneficiary
- First-time recipient
- Device
- Trusted mobile
- Behaviour
- Unusual navigation speed
Behavioural risk score78/100
DecisionVerification
required
required
Behavioural risk detected before releaseVERIF-AI adds a decision layer at the point of payment.
Illustrative example only. No real customer or transaction data is shown.HOW VERIF-AI ASSESSES RISK
Risk is revealed by the pattern, not by a single signal.
A first-time beneficiary is not automatically fraud. Neither is a large payment, a new location or unusual behaviour on its own. VERIF-AI evaluates these signals together against the customer’s normal pattern.
Risk contextSignals assessed together
€Payment amount
▯Device
○Behaviour
⌖Location
▣Payment history
○Beneficiary
HUMAN OVERSIGHT
AI identifies the risk.
People remain in control.
VERIF-AI helps banks identify unusual payment behaviour and prioritise the cases that need attention. Low-risk payments continue normally, while uncertain or high-risk transactions can be verified or escalated to trained specialists before funds are released.
Low risk
→Normal payment flow
Routine activity continues without unnecessary friction.
Medium risk
→Customer verification
Additional checks help confirm that the payment reflects the customer’s genuine intent.
High risk
→Specialist review
High-risk or unclear cases are escalated for human assessment before release.
AI informs the decision. People stay in control.
VALUE FOR BANKS
Prevent fraud before it becomes a loss and reduce the cost that follows.
Stopping a fraudulent payment before release can also reduce reimbursement, investigations, support demand and the wider impact on customer trust.
Lower fraud losses
Intervene before a manipulated payment is released and becomes a financial loss.
Lower operational pressure
Reduce avoidable support calls, complaints, recovery work and fraud investigations.
Stronger customer trust
Protect customers from sophisticated manipulation without adding unnecessary friction.
Better use of fraud specialists
Prioritise human review around transactions carrying the strongest risk signals.
- 01Design →
Define
Agree the transaction scope, risk signals, decision logic and success criteria.
- 02Test →
Pilot
Test VERIF-AI within a controlled transaction segment and operating environment.
- 03Measure →
Validate
Measure fraud detection, false positives, customer friction and operational impact.
- 04Scale →
Scale
Expand into wider fraud and risk operations only when the evidence supports it.
PILOT PROPOSAL
Prove the value in a controlled pilot, then scale with evidence.
Start with a defined transaction segment and clear success criteria. The pilot validates detection quality, customer impact and operational value before broader deployment.
Discuss a pilot
Build confidence before
broader deployment
