Is Your Business Struggling with These Payment Fraud Challenges?
Protect your revenue and reputation by addressing fraud challenges head-on, ensuring secure, compliant, and efficient payment operations.
High Fraud Velocity in Instant Payments
Sub-second clearing leaves no buffer for manual review, so malware, bots, and social-engineering scams can drain accounts before you can react.
Account Takeovers & Synthetic IDs
Fraudsters hijack credentials or fabricate identities to bypass legacy KYC, exposing payment companies to unauthorized transfers and regulatory fines.
Chargebacks and Disputes Surge
Card-not-present fraud, refund abuse, and reseller collusion drive costly chargebacks that eat interchange revenue and strain ops teams.
Fragmented Global Regulations
Keeping pace with PSD2, FedNow, UPI, and FATF rules strains compliance budgets and creates audit gaps across regions and rails.
Empower Your Security with FraudNet's Cutting-Edge Solutions
Secure your transactions, streamline compliance, and reduce fraud losses with FraudNet's comprehensive solutions for payment companies.
Real-Time Transaction Monitoring
Analyze every transaction live; block fraud before authorization completes.
Continuous Entity Screening
Screen merchants/users continuously for AML, KYB, sanctions compliance.
AI-Native Risk Decisioning
Train models on your data for precise, low-friction risk decisions.
Centralized Case Management
One workspace to triage alerts, collaborate, and speed investigations.
Millisecond Fraud Detection
FraudNet instantly evaluates each transaction, halting high-risk activities before any funds exit your system. This not only safeguards your assets but also ensures a seamless experience for genuine customers, bolstering trust and reducing the potential for costly chargebacks.
Adaptive, Client-Trained Models
Harness the power of AI-Native models tailored specifically to your data, minimizing false positives and swiftly adapting to new fraud tactics and seasonal trends. Elevate your fraud prevention strategy with precision and agility, ensuring seamless transactions for your payment company.
Unified Compliance & Fraud Hub
Streamline your operations with our unified dashboard that consolidates screening, alerts, and case workflows. This integration cuts investigation time, simplifies audits, and reduces operational expenses, empowering your team to focus on growth and delivering seamless payment experiences.
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
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FAQs
What is real-time transaction monitoring?
Real-time transaction monitoring is a system used by payment companies to track and analyze transactions as they occur. This process helps in identifying suspicious activities that could indicate fraud. By leveraging advanced algorithms and machine learning, these systems can flag anomalies based on predefined rules and patterns, allowing companies to take immediate action to prevent fraudulent transactions and protect customers' accounts.
How does real-time transaction monitoring benefit payment companies?
Real-time transaction monitoring benefits payment companies by enhancing fraud detection capabilities, reducing financial losses, and improving customer trust. By promptly identifying and addressing suspicious activities, companies can mitigate risks and ensure compliance with regulatory standards. This proactive approach not only safeguards financial assets but also enhances customer satisfaction by providing a secure transaction environment, ultimately fostering customer loyalty and confidence in the company's security measures.
What technologies are used in real-time transaction monitoring?
Real-time transaction monitoring employs various technologies such as machine learning, artificial intelligence, and big data analytics. These technologies enable systems to analyze large volumes of transaction data quickly and identify patterns indicative of fraudulent behavior. Additionally, rule-based systems and behavioral analytics are used to set specific parameters and thresholds for detecting anomalies. By integrating these technologies, payment companies can enhance their ability to detect and prevent fraud effectively.
How do payment companies handle false positives in transaction monitoring?
Payment companies manage false positives by continuously refining their monitoring algorithms and incorporating feedback loops to improve accuracy. Machine learning models are trained to distinguish genuine transactions from suspicious ones more effectively over time. Furthermore, companies may use multi-factor authentication and additional verification steps to validate transactions flagged as suspicious. By balancing security measures with user experience, companies aim to minimize disruptions for legitimate customers while effectively identifying potential fraud.
What regulatory requirements must payment companies adhere to for transaction monitoring?
Payment companies must comply with various regulatory requirements, including Anti-Money Laundering (AML) laws, the Payment Card Industry Data Security Standard (PCI DSS), and the General Data Protection Regulation (GDPR), among others. These regulations mandate that companies implement robust transaction monitoring systems to detect and prevent fraudulent activities. Compliance involves maintaining detailed records of transactions, reporting suspicious activities to relevant authorities, and ensuring customer data privacy and security throughout the monitoring process.
How do machine learning models enhance real-time transaction monitoring?
Machine learning models enhance real-time transaction monitoring by enabling systems to learn from historical data and improve their ability to detect anomalies over time. These models can analyze complex patterns and adapt to new types of fraud, making them more effective than traditional rule-based systems. By identifying subtle indicators of fraud and reducing false positives, machine learning models help payment companies respond swiftly to potential threats, ensuring a more secure transaction environment for customers.