Are High Fraud Rates and Regulatory Hurdles Draining Your Payment Process?
Protect your bottom line by reducing fraud losses and ensuring compliance, all while maintaining smooth and secure transactions.
High Fraud Rates in Real-Time Payments
Sub-second clearing leaves no buffer for manual review, so scams, mule accounts, and card-not-present attacks slip through, driving direct loss and network penalties for payment processors.
Account Takeovers & Synthetic Identities
Fraudsters hijack consumer wallets or spin up fake personas to pass KYC, exploit payout rails, and drain balances—forcing payment companies to cover losses and rebuild customer trust.
Chargebacks & Payment Disputes
Promotion abuse, friendly fraud, and first-party misuse inflate refund volumes, triggering higher scheme fees, operational costs, and degraded issuer relationships for acquirers.
Regulatory Fragmentation
FedNow, PSD2 SCA, 5AMLD, and local AML rules evolve at different speeds, making it costly for payment firms to align controls, prove compliance, and avoid multimillion-dollar fines.
Transform Payments with FraudNet: Real-Time Fraud Solutions
Boost security and compliance effortlessly, minimizing fraud losses and regulatory costs for payment companies.
Real-Time Transaction Monitoring
Scores every payment in ms, blocks anomalies before settlement.
Entity Screening
Instant KYB-AML checks keep onboarding compliant without friction.
AI & ML Risk Decisioning
Custom models learn your data, lift approvals, shrink fraud loss.
Case Management System
Unified console triages alerts, speeding analyst reviews end-to-end.
AI-Native Real-Time Detection
FraudNet's advanced AI analyzes billions of signals in milliseconds, intercepting threats before they impact your ledger. This ensures your authorization rates remain robust, safeguarding your revenue and maintaining strong relationships with issuers, merchants, and cardholders. Stay secure and efficient.
Exceptionally Low False Positives
Our adaptive models significantly reduce unnecessary declines, ensuring legitimate transactions pass smoothly. This not only boosts your transaction volume but also strengthens relationships with issuers, merchants, and cardholders, safeguarding your business and enhancing trust across the payment ecosystem.
Automated, Audit-Ready Compliance
Our built-in rule libraries seamlessly align with PSD2, FATF, and FedNow standards, providing instant compliance with evolving regulations. Generate searchable audit trails effortlessly, minimizing manual reporting and ensuring your payment operations remain both efficient and penalty-free.
Impact & Results
Delivering Results that Matter
We don’t just promise better fraud control—we deliver tangible improvements that protect your business.
97%
Fewer False Positives
Approve more valid transactions confidently.
88%
Fraud Reduction
Experience double-digit reductions in fraud-related chargebacks
60%
Cost Savings
Save time and resources while securing your revenue.
Why FraudNet
Future-Proof Your Fraud & Risk Program
With an integrated platform designed for precision, agility, and impactful results, enabling your team to make smarter decisions, improve operational efficiency, and fuel your business growth.
Customizable & Scalable
No-code rules engine, flexible dashboards, and tailor-made machine learning models that are designed to adapt seamlessly and scale alongside your business.
End-to-End Platform
Unify fraud detection, compliance, and risk management into one powerful solution, saving valuable time and streamlining your operations.
AI Precision You Can Rely On
Reduce false positives, detect and prevent more fraud, and mitigate risk with highly accurate, real-time risk scoring and anomaly detection you can trust.
Real-Time Fraud Intelligence
Leverage advanced analytics, comprehensive reporting, and our Global Anti-Fraud Network to make faster, smarter decisions on the spot.
Testimonials
Real Success From Real Teams
Fraud.net’s flexibility has helped our AfterPay business grow by allowing us to meet our increasingly complex customer and country requirements. Their platform has enabled Arvato to increase our agility and significantly reduce fraud attacks.
Director Risk & Fraud, Arvato
FraudNet's combination of customized machine learning and flexible rules management has been transformative. We've achieved dramatic efficiency gains while maintaining robust fraud protection - a game-changer as we navigate evolving regulatory requirements.
Head of Financial Crime, Countingup
The great usability of Fraud.net is night and day when comparing it to our prior risk prevention platform. Reporting is also faster, more straightforward, and more impactful. With Fraud.net, we can easily visualize and share findings, providing our leadership with a clear understanding of the return-on-investment for our activities in real-time.
Fraud Manager, Global Financial Institution
Speak with our Solutions Expert Today
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
Recognized by Industry Analysts
Related Resources
Other Services
View More
FAQs
What types of payment fraud can detection software identify?
Detection software can identify various types of payment fraud, including credit card fraud, account takeover, phishing attacks, identity theft, and transaction laundering, among others. It uses advanced algorithms and machine learning to analyze transaction patterns and detect anomalies that could indicate fraudulent activity. By continuously learning from new data, AI systems can adapt to emerging fraud tactics, offering a robust defense against evolving threats.
How does AI improve fraud detection compared to traditional methods?
AI improves fraud detection by leveraging machine learning algorithms to analyze vast amounts of data in real-time, identifying patterns and anomalies that might be missed by traditional rule-based systems. Unlike conventional methods, AI can adapt to new fraud tactics as they emerge, reducing false positives and enhancing accuracy. This dynamic approach enables more efficient detection, allowing payment companies to quickly respond to threats while minimizing disruptions to legitimate transactions.
What are the benefits of using AI for fraud automation in payment companies?
Using AI for fraud automation offers several benefits, including increased detection accuracy, reduced false positives, and faster response times. AI systems can process large volumes of transaction data in real-time, identifying potential fraud with greater precision. This leads to enhanced trust and security for customers, reduced operational costs, and improved compliance with regulatory standards. Additionally, AI's ability to continuously learn and adapt ensures that payment companies stay ahead of emerging fraud tactics.
Can AI fraud detection systems operate in real-time?
Yes, AI fraud detection systems are designed to operate in real-time, analyzing transactions as they occur to immediately identify and respond to potential fraudulent activity. By utilizing advanced algorithms and machine learning, these systems can quickly assess transaction data, flagging suspicious activities for further investigation or automatic intervention. This real-time capability is crucial for minimizing financial losses and protecting customer accounts from unauthorized transactions.
What challenges do payment companies face when implementing AI fraud automation?
Payment companies face several challenges when implementing AI fraud automation, including integrating new technology with existing systems, ensuring data privacy and regulatory compliance, and managing the balance between fraud detection and customer experience. Additionally, maintaining and updating AI models to handle evolving fraud tactics requires significant resources and expertise. Companies must also address concerns about the transparency and interpretability of AI decisions to build trust with customers and regulators.
How does AI handle false positives in fraud detection?
AI handles false positives in fraud detection by continuously refining its algorithms to improve accuracy and reduce erroneous alerts. Machine learning models are trained on large datasets to distinguish between legitimate transactions and fraudulent activities more effectively. Feedback loops, where the system learns from past decisions, help in minimizing false positives over time. Additionally, AI systems can incorporate contextual data and multi-layered verification processes to ensure that genuine transactions are not mistakenly flagged as fraudulent.