Talk to a solutions advisor about your fraud and compliance stack.
The case for change
Legacy fraud tools force a trade-off: block more fraud and lose good customers, or approve more and bleed revenue.
The old way
The FraudNet way
What it does
Detection, compliance, and case management working from the same data and the same models.
Instant assessment of transactions and users through AI-powered analysis, so decisions happen in the moment, not after the loss.
Business users create and modify fraud rules without engineering help, so you can adapt to a new fraud pattern the day it appears.
Analyze relationships between entities to expose fraud rings and coordinated schemes that transaction-only tools miss.
Outcomes feed back into supervised machine learning models, so detection accuracy keeps improving as your business changes.
Collective intelligence shared across the platform's user base, giving you visibility into fraud patterns beyond your own data.
Entity screening, transaction monitoring, and verification tools built in, instead of bolted on from another vendor.
Customizable analytics and reporting interfaces with real-time insights for fraud teams and leadership alike.
End-to-end workflow management for investigations, so alerts move from detection to resolution without falling through the cracks.
The flow
A clear path from first step to measurable outcome.
Collect real-time transaction and user data through APIs and SDKs.
Enhance incoming data with signals from the Global Anti-Fraud Network and third-party sources.
Graph Neural Networks and Generative AI models analyze patterns and relationships between entities.
ML model outputs combine with the no-code rules engine to generate risk scores in real time.
The Learning Loop feeds outcomes back into the models, improving accuracy with every decision.
Outcomes
“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.”
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Answers
No. The platform features a low-code/no-code rules engine and flexible dashboards, so business users can build and adjust fraud rules without engineering support, while the AI models handle the heavy analysis.
Customers typically experience a 97% reduction in false positives, an 80% reduction in fraud, and a 20% boost in approval rates.
The Learning Loop continuously incorporates new data and outcomes back into supervised machine learning models, adapting detection accuracy as fraud patterns evolve.
AML and KYC verification, entity screening, and transaction monitoring are all built into the platform, alongside case management for investigations.
Payments, Financial Services, Fintechs, and Commerce, with fraud prevention and risk management customized to each industry's requirements.
It pools fraud patterns and insights across the platform's user base, so every participant benefits from threats detected anywhere in the network, not just in their own data.
Take the next step
Book a call and walk through your fraud, risk, and compliance workflows with a solutions advisor.
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