PaySentinel: Real-time payment fraud detection
Stop fraud before the money moves.
PaySentinel scores every payment transaction in real time and flags likely fraud before it is approved. Built for payment gateways, aggregators and fintechs, it learns from your own transaction history: device behaviour, merchant risk, transaction timing and amount patterns.
Capabilities
What PaySentinel does
Real-time risk scoring
Every transaction gets a fraud score and an approve, review or decline decision.
Behavioural signals
Device velocity, unusual amounts, high-risk merchant categories and late-night patterns.
Built-in drift monitoring
Tracks data, score and performance drift, and tells you when to retrain or roll back.
Production-ready safeguards
Schema validation, training-serving consistency checks and versioned models with rollback.
Try it yourself
Score a payment the way PaySentinel does
Change the transaction and watch the fraud score, the decision and the reasons update instantly.
Approve
Why
Simplified demo model for illustration. PaySentinel learns these patterns from your own transaction history.
Value estimate
What is fraud costing you?
Put in your own volumes. The share prevented is your assumption; a pilot measures the real figure on your data.
How it works
How it works
- 1
Connect
Transactions stream in from your gateway or switch.
- 2
Score
Each one is scored against patterns learned from your own history.
- 3
Decide
An approve, review or decline decision comes back before authorisation.
- 4
Monitor
Drift and performance are tracked, with clear signals to retrain or roll back.
Built for
Who it’s for
- Payment gateways
- Payment aggregators
- Fintechs
- Banks
Getting started
What a pilot looks like
- 6–8 weeks on your own historical data, inside your environment
- Shadow mode: no impact on live systems
- Success criteria agreed upfront, with a clear go or no-go at the end
- Fixed fee, credited in full if you roll out
What data does PaySentinel need?
Your historical transaction records with outcomes (for example chargebacks or confirmed fraud), plus the fields you already capture such as device, merchant, amount and time.
Does it replace our existing rules?
It doesn’t have to. Many teams run it alongside their rules first, compare decisions, then decide how to combine them.
Where does it run?
In your environment or a cloud account you approve. Transaction data doesn’t need to leave your control.
What happens when fraud patterns change?
Drift monitoring flags when incoming data or model performance shifts, so you can retrain, or roll back to a previous model version.
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