Is Your Payment System Vulnerable to These Critical Challenges?
Safeguard your payment ecosystem by addressing fraud, identity threats, disputes, and compliance hurdles with robust, real-time solutions.
High Fraud Rates in Real-Time Payments
Instant payout rails (FedNow, SEPA Instant, UPI) give fraudsters zero settlement lag, leaving payment companies unable to insert manual review, recover funds, or reassure merchants.
Account Takeovers & Synthetic Identities
Criminals hijack consumer wallets or create fake personas that bypass KYC—draining balances, diverting payouts, and triggering costly reimbursement obligations for payment processors.
Chargebacks & Payment Disputes
Card-not-present fraud, promo abuse, and refund scams inflate dispute volumes, raise scheme fees, and jeopardize acquirer risk tiers, squeezing already thin processing margins.
Regulatory Fragmentation
Keeping pace with PSD2 SCA, FATF, 5AMLD, and local instant-payment mandates strains compliance teams, increasing audit exposure and delaying market expansion.
Combat Fraud Instantly with FraudNet's Advanced Solutions
Boost security and compliance, reduce fraud losses, and enhance customer trust with FraudNet's advanced solutions.
Real-Time Transaction Monitoring
Millisecond risk scoring stops suspect payments before authorization.
Entity & Identity Screening
Instant KYB/AML checks flag risky merchants, wallets, and devices.
AI-Native Risk Decisioning
Self-learning models cut fraud while boosting genuine approval rates.
Unified Case Management
One workspace triages alerts, tracks evidence, and speeds audits.
Enhanced Fraud Detection
FraudNet scrutinizes each transaction in real time, using global intelligence and adaptive AI to detect hidden anomalies. This ensures your revenue is protected while maintaining seamless payment processing, allowing you to stay ahead of fraudsters without sacrificing speed or efficiency.
Reduced False Positives
Our advanced machine-learning models dynamically adjust risk thresholds, enabling you to approve more legitimate transactions effortlessly. This not only enhances merchant satisfaction but also significantly reduces operational escalations, allowing your team to focus on growth rather than firefighting fraud.
Operational Efficiency & Compliance
Streamline operations with automated workflows, audit trails, and customizable rules, freeing analysts from tedious manual checks. Stay ahead of regulatory requirements effortlessly, ensuring your team is always prepared for scrutiny while enhancing efficiency in managing compliance and fraud prevention.
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
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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 often use sophisticated algorithms and machine learning models to analyze transaction patterns and user behavior in real-time, allowing for quick identification and prevention of fraudulent activities before they can cause significant financial damage.
How does machine learning enhance fraud detection in real-time?
Machine learning enhances fraud detection by continuously learning from vast amounts of transaction data to identify patterns and anomalies indicative of fraud. These models can adapt to new fraud tactics as they emerge, improving detection accuracy over time. By processing data in real-time, machine learning enables payment companies to quickly flag suspicious transactions, reducing false positives, minimizing fraud losses, and enhancing the overall security of payment systems.
What role does data analytics play in fraud detection?
Data analytics plays a crucial role in fraud detection by analyzing large volumes of transaction data to uncover patterns and trends that might indicate fraudulent behavior. Through advanced statistical techniques and predictive modeling, data analytics helps identify anomalies and generate risk scores for transactions. This allows payment companies to make informed decisions and take immediate action on potentially fraudulent activities, thereby enhancing the efficiency and effectiveness of their fraud detection efforts.
How can payment companies reduce false positives in fraud detection?
Payment companies can reduce false positives by employing a multi-layered fraud detection approach that combines rule-based systems with machine learning algorithms. Regularly updating and refining detection models based on the latest fraud patterns helps improve accuracy. Additionally, incorporating real-time data feeds and customer feedback can fine-tune the system's sensitivity, enabling more precise differentiation between genuine and fraudulent transactions, thereby minimizing the inconvenience caused by false alarms to legitimate customers.
What are the benefits of real-time fraud detection for payment companies?
Real-time fraud detection offers numerous benefits, including the immediate identification and prevention of fraudulent transactions before they are processed, reducing financial losses and protecting the company's reputation. It enhances customer trust by providing a secure transaction environment, which can lead to increased customer satisfaction and loyalty. Furthermore, real-time detection helps streamline operations by reducing the need for manual reviews, allowing fraud analysts to focus on more complex cases.
How do payment companies ensure the privacy of customer data during fraud detection?
Payment companies ensure customer data privacy by implementing robust data protection policies and encryption technologies that secure sensitive information during processing and storage. They adhere to industry standards and regulations, such as PCI DSS, to maintain data integrity and confidentiality. Additionally, companies often employ anonymization techniques and limit access to data, ensuring that only authorized personnel can view customer information. Regular audits and compliance checks further reinforce data privacy measures.