Graph Neural Networks, Generative AI, and supervised ML analyze entity relationships and transaction patterns to catch fraud other systems miss.
97% fewer false positives80% less fraud20% higher approval rates
Cut fraud losses by 80% in 90 days
Custom machine learning, real-time risk scoring, and a global anti-fraud network built for payments, fintech, and financial services teams who need answers in milliseconds, not days.
See how FraudNet fits your fraud stack in a 30-minute walkthrough.
The shift
Legacy fraud tools cost you in two directions
Too many false positives kill customer trust. Too many missed fraud events kill revenue. FraudNet closes both gaps.
The old way
- ×High false positive rates flag legitimate transactions and create customer friction
- ×Manual reviews drain analyst time and slow down legitimate customers
- ×Point solutions for fraud, compliance, and risk don't talk to each other
- ×Reactive systems detect fraud after the money is already gone
- ×Limited fraud intelligence - you only see patterns from your own data
The FraudNet way
- ✓AI-powered risk scoring cuts false positives by 97% so good customers move through fast
- ✓No-code rules engine lets business users adapt detection logic without engineering tickets
- ✓One integrated platform covers fraud detection, entity risk, and AML/KYC compliance
- ✓Real-time scoring and anomaly detection stop fraud before it settles
- ✓Global Anti-Fraud Network pools intelligence across organizations so you learn from fraud you never experienced
Features
Everything your fraud and risk team needs in one platform
From real-time scoring to case management, FraudNet replaces fragmented point solutions with a single, adaptable system.
Custom machine learning models
Graph Neural Networks, Generative AI, and supervised ML analyze entity relationships and transaction patterns to catch fraud other systems miss.
Real-time risk scoring
Instant assessment of every transaction and user through AI-powered analysis, with risk scores returned in milliseconds via RESTful APIs and webhooks.
No-code rules engine
Business users create and modify fraud detection rules without writing code, so your team adapts to new fraud patterns in minutes, not sprint cycles.
Global Anti-Fraud Network
Collective intelligence shared across the FraudNet user base. Your models learn from fraud patterns and attack vectors identified by other organizations.
Learning Loop system
Every decision outcome feeds back into the models. Detection accuracy improves continuously as new data and patterns arrive, with no manual retraining.
Compliance suite
Integrated AML and KYC verification, entity screening, and transaction monitoring keep you compliant across jurisdictions without a separate tool.
Flexible dashboards
Customizable analytics and reporting interfaces give your team real-time visibility into risk trends, model performance, and operational metrics.
Case management
End-to-end workflow management for fraud investigations and resolution tracking, so analysts move from alert to outcome in one workspace.
Your path
From data ingestion to decision in milliseconds
A simple route from setup to steady output.
Ingest and enrich
Real-time transaction and user data flows in through APIs and SDKs, then gets enriched with signals from the Global Anti-Fraud Network and third-party sources.
Analyze with AI
Graph Neural Networks and Generative AI models analyze patterns and entity relationships, while supervised ML scores risk based on historical outcomes.
Decide and act
The decision engine combines ML outputs with your no-code rules to generate a risk score. Real-time decisions route back to your systems via API or webhook.
Learn and improve
The Learning Loop feeds every outcome back into the models, continuously refining detection accuracy without manual intervention.
97%Reduction in false positives
80%Reduction in fraud losses
20%Boost in approval rates
90 daysTypical time to measurable results
Proof
What teams say after switching
“FraudNet flexibility has helped our AfterPay business grow by allowing us to meet our increasingly complex customer and country requirements.”
“FraudNet's combination of customized machine learning and flexible rules management has been transformative.”
FAQ
Questions fraud and risk leaders ask before a call
What results do companies typically see after implementing FraudNet?+
Companies typically experience a 97% reduction in false positives, an 80% reduction in fraud, and a 20% boost in approval rates.
Which industries does FraudNet serve?+
FraudNet primarily serves Payments, Financial Services, Fintechs, and Commerce industries with customized fraud prevention and risk management solutions.
Do we need a data science team to use the platform?+
No. FraudNet features a no-code rules engine and flexible dashboards, making it accessible for business users without technical expertise while still offering custom machine learning models for advanced teams.
How does the Learning Loop system work?+
The Learning Loop continuously adapts and improves detection accuracy by incorporating new data and outcomes, using supervised machine learning and advanced AI to refine fraud prevention without manual retraining.
What AI technologies does FraudNet use?+
FraudNet uses Supervised Machine Learning, Graph Neural Networks, and Generative AI for pattern recognition, entity relationship analysis, and fraud detection.
What compliance capabilities are included?+
FraudNet offers integrated AML and KYC verification, entity screening, and transaction monitoring, all within the same platform as fraud detection.
How does the Global Anti-Fraud Network work?+
The Global Anti-Fraud Network pools fraud patterns and insights across the FraudNet user base, giving every participant access to collective intelligence from fraud events they may never have experienced directly.
Get started
Ready to cut fraud by 80%?
Book a 30-minute call to see how FraudNet fits your fraud stack, your compliance requirements, and your transaction volume.
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