Are These Security Challenges Impacting Your Issuing Bank?
Protect your issuing bank from fraud, enhance cardholder trust, and maintain compliance with evolving security challenges.
Account Takeovers (ATO)
Phishing, credential-stuffing, and SIM swaps let fraudsters hijack cardholder logins, triggering chargebacks, brand damage, and costly re-issuance for you as the issuer.
Synthetic Identity Fraud
Fraudsters blend real and fake data to open new cards, bypassing KYC and leaving you with uncollectible balances that distort credit-risk models.
False Declines
Rigid, static rules reject good transactions, frustrating loyal cardholders and slashing interchange revenue while competitors capture the spend.
Rising Compliance Pressure
Evolving PSD2, AML, and CFPB mandates demand rapid rule updates and airtight audit trails—stretching issuer teams and budgets thin.
FraudNet Solutions: Shield Your Business with Precision Defense
Boost issuer security, reduce fraud losses, enhance compliance, and improve customer satisfaction with FraudNet.
Real-Time Transaction Scoring
Millisecond risk scores block fraud before authorization.
Dynamic Rule Builder
Drag-and-drop logic lets teams tweak rules without code.
Consortium Intelligence Feed
Shared fraud signals update your models automatically.
Comprehensive Compliance Engine
Keeps PSD2, AML, and Reg-E reporting current 24/7.
AI-Native, Adaptive Defense
FraudNet intelligently evolves with every issuer transaction, automatically fine-tuning risk thresholds in real time. This means you stay ahead of emerging fraud tactics without the need for manual adjustments, ensuring seamless protection and peace of mind for you and your cardholders.
Zero-Code Customization
Empower your team with a user-friendly interface that allows business users to swiftly create, test, and deploy fraud rules. This zero-code approach accelerates response times from weeks to minutes, minimizing IT dependency and enhancing your operational efficiency.
Precision That Protects Revenue
Experience unparalleled precision with our high-fidelity risk scoring, minimizing false positives and maximizing legitimate approvals. Enhance customer satisfaction, boost loyalty, and capture additional interchange revenue by ensuring your cardholders' transactions are seamlessly processed without unnecessary declines. Protect your reputation and your bottom line.
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 are issuer adaptive fraud rules?
Issuer adaptive fraud rules are dynamic algorithms used by financial institutions to detect and prevent fraudulent transactions. Unlike static rules, these adaptive rules continuously learn and adjust based on new data patterns and behaviors. This adaptability allows issuers to respond more effectively to emerging fraud trends, minimizing false positives and enhancing the accuracy of fraud detection. By leveraging machine learning and AI, these rules can provide real-time insights and decisions.
How do issuer adaptive fraud rules differ from traditional fraud detection methods?
Traditional fraud detection methods rely on static, predefined rules that can quickly become outdated as fraud tactics evolve. In contrast, issuer adaptive fraud rules use machine learning to continuously learn from new transaction data, allowing them to adapt to changing fraud patterns. This leads to more accurate detection, fewer false positives, and a more robust defense against sophisticated fraud schemes, as they can identify subtle anomalies and emerging threats that static rules might miss.
What role does machine learning play in issuer adaptive fraud rules?
Machine learning is central to issuer adaptive fraud rules as it enables the system to automatically learn and improve from experience without being explicitly programmed for every scenario. By analyzing vast amounts of transaction data, machine learning models can identify patterns and anomalies that indicate fraudulent activity. This continuous learning process helps issuers anticipate and respond to new fraud tactics, ensuring that the rules remain effective over time and reducing dependency on manual rule updates.
How do issuer adaptive fraud rules balance fraud detection with customer experience?
Issuer adaptive fraud rules aim to strike a balance between effective fraud detection and maintaining a positive customer experience by minimizing false positives. By accurately identifying fraudulent transactions while allowing legitimate ones to proceed without interruption, these rules help reduce the number of false declines that can frustrate customers. Additionally, adaptive rules can provide insights that help issuers customize fraud prevention strategies to individual customer profiles, ensuring a more personalized and frictionless experience.
Can issuer adaptive fraud rules help with real-time transaction monitoring?
Yes, issuer adaptive fraud rules are designed to support real-time transaction monitoring. By leveraging machine learning and AI, these rules can process and analyze transaction data as it occurs, identifying suspicious activities instantaneously. This real-time capability allows issuers to take immediate action, such as flagging or blocking potentially fraudulent transactions, thereby reducing the risk of financial loss and enhancing the security of the payment ecosystem.
What are some challenges associated with implementing issuer adaptive fraud rules?
Implementing issuer adaptive fraud rules can present several challenges, including the need for high-quality data, integration with existing systems, and ensuring regulatory compliance. The effectiveness of adaptive rules heavily depends on the availability of diverse and accurate transaction data to train the models. Additionally, integrating these advanced systems with legacy infrastructure may require significant IT resources and expertise. Finally, issuers must ensure that their fraud detection practices comply with industry regulations and data privacy laws, which can add complexity to deployment.