We built a real-time AI transaction scoring layer that weighs spending behaviour, location and network-wide anomalies to authorise or decline payments, and updates itself as fraud changes.
Industry: FinTech & Financial ServicesService: AI & Automation
Overview
Client
Mastercard
Global payments network
Engagement snapshot
The challenge
Fraud patterns change quickly and differ by region, and rule engines needed manual updates every time a new pattern appeared.
Any extra checks had to fit inside the time a payment takes to authorise, so cardholders would not notice them at checkout.
New fraud vectors appeared faster than rules could be rewritten.
Every check had to finish within the authorisation window.
Over-cautious rules risked declining genuine payments.
The solution
Gradient-boosted models weigh spending behaviour, location data and network-wide anomalies for each transaction.
Each payment is authorised or declined in under 50 milliseconds.
Automated pipelines and active learning retrain the model on emerging fraud vectors.
Key features
Every transaction is scored and authorised or declined in under 50 milliseconds.
Business impact
Transactions authorised in under 50ms
The model weighs spending behaviour, location data and network-wide anomalies together.
Business impact
Precise fraud decisions
Pipelines retrain the model on emerging fraud vectors without manual rule rewrites.
Business impact
Fraud logic updates itself
Before and after
Before
Rewritten by hand
Each new fraud pattern needed a manual rule change.
After
Updated automatically
Active learning adapts to new fraud vectors.
Before
Constrained checks
Extra checks risked slowing authorisation.
After
Under 50ms
Every payment is scored within the authorisation window.
Before
Friction at checkout
Broad rules risked declining genuine payments.
After
Protected without friction
Precise scoring keeps genuine payments flowing.
Results
Under 50ms Authorisation
Transactions are authorised in under 50 milliseconds.
Before: Constrained ChecksAfter: Real-Time Scoring
No Manual Rule Rewrites
The engine adapts to new fraud vectors without manual rule updates.
Before: Hand-Written RulesAfter: Active Learning
Protection Without Friction
Cardholders are protected globally without extra friction at checkout.
Before: Broad RulesAfter: Precise Scores
Testimonial
“Authorizing transactions in under 50 milliseconds requires extreme precision. This AI scoring engine adapts to new fraud vectors autonomously, protecting our cardholders globally without adding friction to their checkout experience.”
Claire Le GalSenior Vice President of Fraud Intelligence, Strategy & Cyber Products, MastercardKey results
Trust and control
Every authorisation and decline is recorded with its score.
TRACEABLEModel behaviour is watched as it learns from new data.
MONITOREDRetraining runs through automated, tested pipelines.
CONTROLLEDScoring is designed for global payment volumes.
SCALABLEFaster delivery
Starting from tested components let the team spend its time on speed and on the network's own fraud signals.
Tested streaming components kept scoring inside tight time limits.
A reusable active learning loop retrains on new fraud data.
Shared behavioural features are computed once and reused.
Every score and decision is recorded.
We built a real-time AI transaction scoring and fraud prevention layer. Gradient-boosted models analyse spending behaviour, location data and network-wide anomalies to authorise or decline payments globally, while automated data pipelines and active learning loops keep training the model on emerging fraud vectors without manual rule-engine updates.
Transactions are authorised in under 50 milliseconds, inside the normal authorisation window. That speed lets every payment be scored precisely without adding friction to the cardholder's checkout, so genuine payments go through quickly while suspicious ones are declined before any money moves, anywhere in the world.
Automated data pipelines and active learning loops retrain the model on emerging fraud vectors, including new geopolitical and local threats. The fraud logic updates itself instead of waiting for manual rule rewrites, so protection keeps pace with fraud as it changes across regions and payment channels.
If your fraud rules can't keep up with new threats, we can build AI scoring that learns as fraud changes.