Are Real-Time Payment Frauds Draining Your Resources and Damaging Your Reputation?
Protect your bottom line and reputation by tackling fraud, safeguarding transactions, and enhancing trust in real-time payments.
High Fraud Rates in Real-Time Payments
Instant payouts give criminals a zero-window to intercept, letting scams, mule accounts, and CNP fraud drain funds before manual review can catch them—spiking write-offs for payment processors.
Account Takeovers & Synthetic Identities
Fraudsters hijack stored cards or craft fake personas to pass basic KYC, then funnel large volumes through your rails, triggering reputational damage and costly reimbursement obligations.
Chargebacks & Payment Disputes
Card-not-present abuse, friendly fraud, and promo exploitation inflate reversal ratios, raise scheme fees, and threaten acquirer relationships in high-velocity transaction environments.
Regulatory Fragmentation
FedNow, PSD2, 5AMLD, and UPI all demand region-specific controls; juggling audits, reporting formats, and rule updates strains compliance teams and slows geographic expansion.
Defeat Fraud Instantly with FraudNet Solutions
Protect your resources and reputation with FraudNet's swift, comprehensive fraud prevention for payment companies.
Real-Time Transaction Monitoring
Score every authorization in <300 ms, blocking high-risk flows instantly.
Ongoing Entity Screening
Continuously scan merchants and payees against AML, KYB, and sanctions lists.
AI & ML Risk Decisioning
Adaptive models cut false positives while surfacing emerging fraud patterns.
Unified Case Management
Prioritize, investigate, and document alerts in one audit-ready workspace.
AI-Native Millisecond Scoring
FraudNet meticulously analyzes hundreds of signals per transaction, empowering payment companies to confidently approve legitimate payments while intercepting fraudulent activities before funds exit your system. This robust protection ensures smooth user experiences without introducing any additional checkout friction.
Continuous Merchant & User Screening
Ensure compliance and stay ahead of risks with automated KYB/AML checks from onboarding through the entire customer lifecycle. Detect and flag sanctioned or volatile entities instantly, empowering your team to be proactive in maintaining security and regulatory adherence effortlessly.
Centralized Investigations Workspace
Streamline your fraud prevention with a centralized dashboard that integrates alerts, evidence, and automation. Empower your analysts to swiftly resolve cases, reduce operational costs, and ensure compliance with seamless, regulator-ready audit trails—all while safeguarding your payment ecosystem.
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 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. These systems use advanced algorithms and machine learning models to analyze transaction patterns and flag anomalies. By evaluating factors such as transaction velocity, geographic location, and historical spending habits, these tools can effectively differentiate legitimate transactions from potentially fraudulent ones, helping payment companies mitigate fraud risks.
How do machine learning models help in detecting high-volume transaction fraud?
Machine learning models assist in detecting high-volume transaction fraud by analyzing large datasets to identify patterns and anomalies that may indicate fraudulent activity. These models continuously learn from new data, improving their accuracy over time. By leveraging supervised and unsupervised learning techniques, they can detect subtle and complex fraud patterns that traditional rule-based systems might miss, enabling real-time and more precise fraud detection for payment companies.
What are some common indicators of high-volume transaction fraud?
Common indicators of high-volume transaction fraud include sudden spikes in transaction volume, unusual geographic locations, frequent small transactions that avoid detection thresholds, and multiple transactions in a short time frame. Other signs include mismatched billing and shipping addresses, transactions from high-risk IP addresses, and repeated declined transaction attempts. Recognizing these patterns helps payment companies to flag suspicious activity and take preventative measures.
How can payment companies minimize false positives in fraud detection?
Payment companies can minimize false positives by refining their fraud detection algorithms and incorporating a layered approach that combines rule-based systems with machine learning. Regularly updating models with the latest fraud trends, transaction data, and customer behavior patterns can enhance accuracy. Additionally, implementing a feedback loop where flagged transactions are reviewed and used to adjust detection parameters can significantly reduce false positives, ensuring legitimate transactions are not inadvertently blocked.
What role does real-time monitoring play in preventing payment fraud?
Real-time monitoring is crucial in preventing payment fraud as it allows for immediate detection and response to suspicious transactions. By continuously analyzing transaction data as it occurs, payment companies can quickly identify anomalies and take action, such as flagging, holding, or declining transactions. This proactive approach helps to minimize potential losses and prevents fraudulent transactions from being processed, thereby protecting both the company and its customers.
How do fraudsters exploit high-volume transactions for fraudulent activity?
Fraudsters exploit high-volume transactions by blending fraudulent charges with legitimate transactions to avoid detection. They may use techniques like transaction splitting, where they break down a large fraudulent transaction into smaller ones, or employ botnets to execute high-frequency, low-value transactions. By mimicking normal transaction patterns, they aim to bypass security measures. Payment companies need sophisticated detection systems to identify these subtle anomalies and prevent potential fraud.