Are Rising Chargebacks and Fraudulent Transactions Draining Your Resources?
Gain control over chargebacks and fraud with real-time insights, protecting your margins and strengthening merchant relationships.
High Chargeback Exposure
Without granular, real-time oversight, acquirers absorb rising chargebacks, network fines, and reserve demands that erode margins and strain merchant relationships.
Onboarding Risk of High-Risk Merchants
Limited real-time KYB insight can let illicit or non-compliant merchants slip into your portfolio, amplifying fraud losses and regulatory scrutiny down the road.
Fraudulent Transactions Across Portfolios
Card-not-present fraud quickly propagates across thousands of merchants; stale batch reporting delays interdiction and drives up fraud reimbursement costs.
Lack of Merchant Behavior Visibility
Siloed data obscures sudden spikes in refunds, sales volume, or product mix, preventing acquirers from intervening before risk thresholds are breached.
FraudNet: Instantly Detect and Defend Against Payment Risks
Optimize resources with FraudNet: detect fraud early, reduce chargebacks, and enhance merchant relationships effortlessly.
Real-Time Transaction Monitoring
Instant analytics flag risky payments before settlement, curbing chargebacks.
Adaptive Policy Rules
Editable rules let you fine-tune thresholds without code or downtime.
Comprehensive Case Management
Auto-create cases, assign tasks, and track outcomes in one console.
Ongoing Screening
Continuous merchant screening against sanctions and watchlists; alerts you fast.
AI-Native Real-Time Detection
FraudNet's lightning-fast AI scores every authorization in milliseconds, intercepting high-risk CNP and first-party fraud before settlement. This proactive approach safeguards your interchange revenue and fortifies merchant relationships, allowing you to maintain profitability and trust in a competitive landscape.
Merchant-Centric Risk Dashboards
Gain complete transparency into each merchant's chargebacks, refunds, and behavior changes with our Merchant-Centric Risk Dashboards. This enables you to engage proactively, optimize reserve management, and strengthen your merchant relationships by addressing issues before they escalate.
Automated Compliance & Workflow
Streamline compliance with built-in PCI and card-network rule libraries that automatically document decisions. Efficiently manage cases with organized queues, detailed notes, and transparent audit trails, reducing investigation time and minimizing regulator inquiries, so you can focus on growing your merchant portfolio.
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 Acquirer real-time transaction monitoring?
Acquirer real-time transaction monitoring is a process used by acquiring banks to observe and analyze payment transactions as they occur. This system helps in detecting potentially fraudulent activities by examining patterns, anomalies, and predefined rules. By leveraging advanced analytics and machine learning, acquirers can quickly identify suspicious transactions, enabling them to take immediate action to prevent fraud and minimize financial loss.
Why is real-time monitoring important for acquirers?
Real-time monitoring is crucial for acquirers as it allows them to detect and respond to fraudulent activities instantly. This immediate response capability minimizes financial losses and protects merchants and cardholders from fraud. Additionally, real-time monitoring helps acquirers maintain compliance with industry regulations, improve trust with stakeholders, and enhance overall transaction security by continuously adapting to emerging fraud patterns and techniques.
How does machine learning enhance transaction monitoring?
Machine learning enhances transaction monitoring by analyzing vast amounts of transaction data to identify patterns and anomalies that may indicate fraud. Unlike traditional rule-based systems, machine learning models can adapt and learn from new fraud patterns, improving their predictive accuracy over time. This allows acquirers to detect subtle, sophisticated fraud attempts and reduce false positives, ultimately providing more reliable and efficient real-time fraud detection.
What are the challenges faced in real-time transaction monitoring?
Real-time transaction monitoring faces several challenges, including the need to process vast volumes of data quickly, balancing detection accuracy with false positive rates, and adapting to constantly evolving fraud tactics. Additionally, integrating monitoring systems with existing infrastructure and ensuring compliance with regulatory standards can be complex. Acquirers must also manage the costs associated with technology upgrades and skilled personnel to ensure effective monitoring capabilities.
How do acquirers balance fraud detection with customer experience?
Acquirers balance fraud detection with customer experience by implementing sophisticated algorithms that minimize false positives, thereby reducing legitimate transaction declines. Real-time monitoring systems are designed to evaluate risk without disrupting the transaction flow unnecessarily. By leveraging machine learning and customer behavior analysis, acquirers can refine their fraud detection processes to ensure that genuine transactions are approved swiftly, maintaining a seamless customer experience.
What role do regulatory requirements play in real-time transaction monitoring?
Regulatory requirements play a significant role in shaping real-time transaction monitoring practices. Regulations such as PCI DSS, PSD2, and AML laws mandate acquirers to implement robust monitoring systems to detect and prevent fraudulent activities. Compliance ensures that acquirers maintain security standards, protect customer data, and avoid legal penalties. These regulations also drive the adoption of advanced monitoring technologies and encourage continuous improvement in fraud detection methodologies.