Adyen’s Real-Time Fraud Detection: A Machine Learning Success Story

Static Fraud Rules Falling Short: Manual fraud rules couldn't keep up with dynamic fraud patterns. Frequent updates led to increased operational load and lag in response times.

High False Positive Rate: A significant percentage of legitimate customers were being blocked, especially during peak seasons, directly impacting revenue and customer experience.

Lack of Cross-Channel Insights: The client operated multiple platforms, but fraud signals weren’t connected across channels, making it difficult to trace coordinated fraudulent activity.

Increased First-Party Fraud & Policy Abuse: With refund abuse and chargeback fraud on the rise, the client needed a smarter system that could differentiate between genuine user errors and intentional misuse.

Leverage Network-Wide Intelligence: Adyen’s global network provided insights from billions of transactions, helping the client stay ahead of emerging fraud patterns seen across other merchants and regions.

Utilise Graph-Based Machine Learning: The solution identified indirect fraud links, such as shared devices, geolocation anomalies, and behavioral mismatches, that manual systems would have missed.

Enable Real-Time Decisioning: Transactions were evaluated in real time using ML risk scores, allowing instant approvals or declines without adding latency.

Customise Fraud Controls Per Market: We helped the client configure dynamic rules based on market risk levels, customer behavior, and historical fraud signals, all within Adyen’s configurable policy layer.

Reduce Manual Reviews via Risk Scoring: High-risk transactions were automatically routed to secondary validation or declined, significantly reducing the manual review workload for the internal team.

Data Mapping & Pattern Analysis: We worked with the client’s data and Adyen logs to identify fraud hotspots, seasonal trends, and past failure points.

Integration & Sandbox Testing: Adyen’s API and dashboard were integrated with the client’s backend system for seamless data exchange. Extensive testing ensured transaction speed and UX were unaffected.

Policy Optimisation & Custom Rule Creation: With Adyen’s support, we co-developed custom policies based on real-world abuse scenarios like “card testing,” “family plan fraud,” and “refund loops.”

Live Deployment & Continuous Optimisation: The system was launched with active monitoring dashboards, periodic audits, and model updates in sync with evolving fraud behavior.

65% Reduction in Fraud Losses: Real-time detection and network-based insights helped significantly curb direct losses due to fraud.

30% Fewer False Positives: Better distinction between legitimate and suspicious activity improved approval rates, especially for returning customers.

80% Drop in Manual Review Volume: Risk scoring and automation drastically reduced the dependency on human analysts.

Increased Customer Trust & Retention: Users experienced faster checkouts and fewer unnecessary verifications, leading to improved trust and loyalty.

Adyen’s RevenueProtect ML Engine: The system combines global network signals, graph-based models, and AI-driven policies for dynamic fraud prevention.

Custom Rule Layer: We built rules for edge-case scenarios and added market-specific thresholds to address local nuances.

Seamless Integration & Monitoring: Real-time dashboards allowed stakeholders to monitor fraud trends and adjust controls without coding changes.

Collaborative Expertise: Our fraud consultants worked directly with Adyen’s team and the client’s risk managers to ensure alignment, speed, and precision throughout the rollout.