Are You Struggling with These Cross-Border Payment Challenges?
Streamline operations, reduce compliance risks, and enhance fraud detection to boost efficiency and competitiveness in global payment corridors.
Excessive False-Positive Alerts
Legacy rule sets misread cultural spending patterns, currency conversions, and time-zone spikes, forcing cross-border payment teams to review mountains of harmless transactions, slowing settlement and frustrating global merchants.
Fragmented Regulatory Landscape
You juggle conflicting KYC, AML, and sanctions rules across dozens of jurisdictions. Constant rule changes demand nonstop updates, increasing the risk of fines and customer friction when controls lag.
Evasive, Multi-Jurisdiction Fraud
Money-mule rings and synthetic IDs hop between regions and PSPs, hiding behind differing privacy laws. Traditional monitoring lacks the shared intelligence to link dispersed signals and stop coordinated attacks.
Costly Manual Investigations
Analysts sift through multilingual evidence, multiple data silos, and fragmented case tools. High labor spend and slower SLAs erode thin FX margins and weaken competitiveness in real-time corridors.
Transform Fraud Detection with FraudNet's Cutting-Edge Solutions
Boost efficiency and security, streamlining compliance and fraud prevention for seamless cross-border transactions.
AI-Native Transaction Scoring
Learns local patterns, reducing benign alerts in milliseconds.
Unified Global Watchlists
Auto-syncs sanctions and PEP data across all corridors.
Behavioral Analytics Layer
Links devices, velocity, and geos to reveal mule networks.
Streamline Compliance Reporting
Detailed, unified data makes SAR/STR filing a breeze.
Precision, AI-Native Scoring
FraudNet's AI-driven scoring system blends supervised and unsupervised learning to adapt to new currencies, local holidays, and specific spending patterns, reducing false positives by up to 70%. Maintain high catch rates while streamlining your cross-border payment operations.
Global Consortium Intelligence
Gain an edge over multi-jurisdictional fraud with access to a vast network of cross-industry fraud signals. Detect mule accounts and synthetic IDs early, stopping them before they infiltrate your platform, ensuring secure and seamless cross-border transactions.
No-Code Workflow Automation
Streamline your compliance operations with drag-and-drop rules, instant case routing, and auto-populated audit trails. Resolve alerts 60% faster, ensuring airtight evidence for regulators and internal audits. Enhance efficiency and maintain robust oversight, all while empowering your cross-border payment team.
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 cross-border false positive reduction?
Cross-border false positive reduction refers to the process of minimizing incorrect fraud alerts in international transactions. These false positives occur when legitimate transactions are mistakenly flagged as fraudulent due to the complexities of cross-border payments, such as differing regulations, currency exchanges, and cultural variances. Reducing false positives is crucial for improving customer experience and operational efficiency, as well as avoiding unnecessary transaction declines.
Why are false positives common in cross-border transactions?
False positives are common in cross-border transactions due to the complexities of operating across different countries and regions. Factors such as varying regulatory environments, diverse consumer behaviors, and currency fluctuations can trigger fraud detection systems. Additionally, cross-border transactions often involve multiple intermediaries, increasing the likelihood of data mismatches or anomalies that can be misinterpreted as fraudulent activities.
What are the consequences of high false positive rates in cross-border payments?
High false positive rates can lead to several adverse consequences, including customer dissatisfaction due to declined legitimate transactions, loss of sales, and reputational damage. They can also increase operational costs as businesses spend more resources on manual reviews and customer support to address flagged transactions. Over time, consistently high false positive rates can erode trust in the payment provider's ability to process transactions efficiently and accurately.
How can machine learning help reduce false positives in cross-border transactions?
Machine learning can significantly reduce false positives by analyzing vast amounts of transaction data to identify patterns and anomalies more accurately. These algorithms can adapt to new fraud tactics and learn from historical data, improving detection precision over time. In cross-border transactions, machine learning models can incorporate diverse data points such as geolocation, transaction history, and behavioral analytics to distinguish legitimate transactions from fraudulent ones more effectively.
What role does data quality play in reducing false positives?
Data quality is crucial in reducing false positives, as accurate and comprehensive data helps fraud detection systems make more informed decisions. High-quality data ensures that transaction information is up-to-date, consistent, and free from errors. This enables better pattern recognition and anomaly detection, reducing the likelihood of legitimate transactions being flagged as fraudulent. Regular data audits and validations can help maintain data integrity and enhance the effectiveness of fraud prevention measures.
What strategies can businesses implement to reduce false positives in cross-border payments?
Businesses can implement several strategies to reduce false positives, such as enhancing data accuracy, leveraging machine learning models for better fraud detection, and refining fraud rules to cater to specific regional patterns. Collaborating with local financial institutions to understand regional transaction behaviors and deploying multi-layered authentication processes can also help. Regularly updating fraud detection algorithms based on emerging threats and customer feedback ensures that systems remain effective and adaptive.