Is Your Business Vulnerable to These Critical Risks?
Safeguard your brand and streamline operations by addressing key threats in agent networks, compliance, and fraud detection.
Agent & Partner Risk Exposure
Your business depends on far-flung agent networks. A single rogue or compromised partner can funnel illicit funds through your corridors, damaging brand trust and triggering costly clawbacks.
Cross-Border AML / KYB Complexity
Every corridor brings different AML rules, sanctions lists, and KYB documentation. Manual reviews slow onboarding, invite human error, and leave you vulnerable to regulatory fines.
Money Muling via Legitimate Agents
Fraudsters recruit real agents to layer transactions just below reporting thresholds, masking originators and exposing you to unwitting money-laundering violations.
Excessive False Positives
Static rules flag normal peak-season remittances as suspicious, freezing family payouts, driving call-center volume, and straining thin compliance teams.
FraudNet: Revolutionize Compliance with Real-Time Solutions
Enhance security, streamline compliance, and safeguard brand reputation with FraudNet's advanced solutions for remittance companies.
Real-Time Monitoring Engine
Live scoring of every send & payout
Automated Compliance Reporting
Instant SAR/STR file generation
AI-Native Fraud Analytics
Detect hidden agent collusion
Scalable Cloud Platform
Elastic capacity for peak volumes
Adaptive, Corridor-Aware Scoring
FraudNet intuitively adapts to local remittance patterns, drastically reducing fraud losses while ensuring genuine family transfers are uninterrupted. By understanding regional nuances in real time, it provides a seamless experience for your customers and protects your business from fraudulent activities.
Unified Agent & Transaction View
Gain complete oversight with our unified dashboard that consolidates sender, receiver, and agent risk signals. This streamlined view accelerates investigations, minimizes escalations, and empowers your team to act swiftly, enhancing efficiency and safeguarding your operations against potential threats.
Exceptionally Low False Positives
Our precision models slash manual alerts by up to 70%, empowering your compliance team to concentrate on genuinely suspicious activities. This means fewer disruptions and more efficient operations, ensuring you can confidently manage risk while facilitating seamless remittance services for your clients.
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 in remittance?
Real-time transaction monitoring in remittance involves continuously analyzing transactions as they occur to detect any suspicious activities. This process uses advanced analytics and algorithms to identify potentially fraudulent transactions, ensuring compliance with regulatory requirements and enhancing the security of financial transfers. By monitoring transactions in real-time, remittance companies can quickly respond to and prevent fraudulent activities, protecting both the company and its customers.
Why is real-time transaction monitoring important for remittance companies?
Real-time transaction monitoring is crucial for remittance companies to prevent fraud, ensure compliance with anti-money laundering (AML) regulations, and protect customer funds. By identifying suspicious activities promptly, companies can mitigate risks, avoid financial losses, and maintain their reputation. Additionally, real-time monitoring helps in adhering to regulatory requirements, thus avoiding potential fines and sanctions, and building trust with customers by ensuring their transactions are secure.
How does real-time transaction monitoring work?
Real-time transaction monitoring works by using advanced software systems that analyze transaction data as it is processed. These systems use machine learning algorithms and rule-based engines to identify patterns indicative of fraudulent activities. Suspicious transactions are flagged for further investigation, allowing remittance companies to take swift action. The systems are often integrated with other data sources to enhance accuracy and are continually updated to reflect new fraud trends and regulatory changes.
What are some challenges faced in real-time transaction monitoring?
Some challenges in real-time transaction monitoring include handling large volumes of data, reducing false positives, and adapting to evolving fraud tactics. Balancing speed with accuracy is crucial to avoid disrupting legitimate transactions while effectively detecting suspicious ones. Additionally, ensuring system scalability and staying compliant with varying international regulations can be complex. Continuous updates and staff training are necessary to keep up with technological advancements and emerging fraud patterns.
How can remittance companies improve their real-time transaction monitoring systems?
Remittance companies can enhance their real-time transaction monitoring by investing in advanced analytics and machine learning technologies, which improve the accuracy of fraud detection. Regularly updating monitoring rules and algorithms to reflect evolving fraud tactics is essential. Collaborating with industry peers for shared insights and threat intelligence can also be beneficial. Additionally, providing ongoing training for staff and ensuring seamless integration with other security systems can help maintain robust monitoring processes.
What role does machine learning play in real-time transaction monitoring?
Machine learning plays a critical role in enhancing the effectiveness of real-time transaction monitoring by enabling systems to learn from historical data and identify complex fraud patterns. It allows for the development of predictive models that can detect anomalies and unusual behaviors more accurately than traditional rule-based systems. By continuously learning from new data, machine learning algorithms improve over time, reducing false positives and enhancing the ability to detect emerging threats in real-time.