Are Operational Inefficiencies and Customer Frustrations Sabotaging Your Success?
Streamline operations and enhance customer experiences by reducing false positives and improving transaction approvals, boosting your bottom line.
Operational Inefficiencies
High false-positive rates force issuer fraud teams to review thousands of clean authorizations daily, bloating headcount, lengthening investigation queues, and inflating compliance costs.
Customer Frustration
Cardholders experience embarrassing declines at checkout when legitimate spend is blocked, driving calls to support, abandoned carts, and migration to competing cards.
Financial Losses
Each rejected good transaction reduces interchange income and strains co-brand merchant ties, while excess manual reviews drain budget.
Reputational Damage
Persistent misclassifications erode issuer credibility with merchants, networks, and regulators, undermining brand trust and Net Promoter Scores.
Transform Fraud Prevention with Fraudnet's Cutting-Edge Solutions
Boost issuer efficiency and cardholder satisfaction by reducing false positives and enhancing transaction accuracy.
AI-Native Real-Time Scoring
Millisecond scoring slashes false positives before authorization completes.
Behavioral Analytics Layer
Tracks spend behavior to separate true anomalies from loyal usage.
No-Code Rules Engine
Effortlessly create or update rules to keep up with new fraud patterns, no coding required.
Global Fraud Intelligence Hub
Shares cross-issuer insights to stop emerging fraud without overblocking.
Enhanced Approval Rates
Boost your approval rates by confidently approving more legitimate transactions, enhancing interchange revenue while keeping fraud losses in check. This balance ensures top-line growth and elevates cardholder satisfaction, reinforcing your position as a trusted issuer in the market.
Lower Operating Cost
Streamline your operations with unified workflows and automated evidence collection. High-precision alerts drastically cut down manual review time per case, empowering your analysts to handle increased volumes efficiently, all without the need to expand your team. Boost productivity with ease.
Audit-Ready Compliance
Ensure seamless compliance with PSD2, PCI DSS, and network mandates through comprehensive case histories, rule-change logs, and instant reports. Streamline your audit preparation, minimize penalty risks, and focus on what truly matters—delivering exceptional service to your cardholders.
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 issuer false positive reduction in the context of payment processing?
Issuer false positive reduction refers to minimizing incorrect alerts or flags in fraud detection systems where legitimate transactions are mistakenly identified as fraudulent. This is crucial for issuers because high false positive rates can lead to customer dissatisfaction, increased operational costs, and potentially lost revenue. Effective reduction strategies involve refining algorithms and using more sophisticated data analytics to accurately differentiate between genuine and fraudulent transactions.
Why is reducing false positives important for issuers?
Reducing false positives is important for issuers as excessive false alerts can lead to customer frustration due to declined legitimate transactions. This can damage the issuer's reputation and customer trust. Additionally, handling false positives requires resources, increasing operational costs. By minimizing false positives, issuers can improve customer satisfaction, reduce unnecessary expenses, and focus resources on investigating actual fraudulent activities.
What are common strategies to reduce false positives in fraud detection?
Common strategies to reduce false positives include using machine learning models that learn and adapt from historical transaction data, implementing multi-layered authentication processes, and leveraging real-time analytics. Additionally, issuers can refine risk scoring models and incorporate more contextual data, such as geolocation and transaction patterns, to improve accuracy. Collaboration with external data sources and continuous model evaluation are also key to improving detection precision.
How does machine learning help in reducing false positives?
Machine learning helps in reducing false positives by analyzing vast amounts of transaction data to identify patterns and anomalies with high precision. These models can learn from past transactions, adjusting to new fraud patterns and distinguishing between legitimate and suspicious activities more accurately. Over time, machine learning algorithms become more adept at predicting fraudulent behavior, thereby reducing the rate of false positives and improving overall fraud detection efficiency.
What role does data quality play in reducing false positives?
Data quality plays a crucial role in reducing false positives, as the accuracy and completeness of data directly impact the effectiveness of fraud detection systems. High-quality data ensures that algorithms can make informed decisions, distinguishing between legitimate and fraudulent transactions more effectively. Inaccurate or incomplete data can lead to incorrect assessments and higher false positive rates. Therefore, maintaining robust data management practices is essential for issuers seeking to enhance their fraud detection capabilities.
Can collaboration with other financial institutions help in reducing false positives?
Yes, collaboration with other financial institutions can help reduce false positives. By sharing information about fraud patterns, emerging threats, and best practices, issuers can enhance their detection models. Collaborative efforts can include participating in industry forums, contributing to shared databases of known fraudsters, and leveraging consortium-based machine learning models. Such collaboration helps issuers stay informed about evolving fraud tactics and improve their fraud detection systems' accuracy and efficiency.