Our analytical philosophy and commitment to better fraud detection
Fraud analytics: how we go from data to insight
1
Ingest and prep your ecommerce data
Bring every relevant data source together, from order flows to device fingerprints
We start by connecting all available data sources—orders, payment info, device IDs, and behavioral logs. Every data stream gets standardized and reviewed for completeness. Clean data reduces false positives and helps our models see trends, not noise. Manual audits help catch gaps before they cause trouble.
Data ingestion usually takes less than a day, with close support from your IT contact. We ensure privacy compliance at every stage.
Spend extra time on your initial data mapping. The better the input, the sharper the signals.
2
Spot patterns and surface anomalies
Detect the unusual by comparing activity to your unique history
Our analytics engine uses pattern recognition and anomaly detection, comparing each transaction to business-specific norms. When something strays—a new location, odd timing, or outlier value—it’s flagged. Our team reviews edge cases and updates logic as new fraud tactics appear.
Pattern models are tuned weekly, based on new flagged transactions and client feedback. No model is left on autopilot.
Review flagged cases together—what looked risky last month may now be normal for your business.
3
Flag, explain, and learn from risk signals
Get fast, detailed alerts with clear explanations
We send alerts instantly to your dashboard and, if needed, to your team by email or SMS. Each alert includes the context and rationale. Reviewing these together sharpens future detection and keeps everyone on the same page. Actionable insight, not just noise.
Alerting is fully configurable—set who gets what, when, and how. Feedback on alerts feeds back into our learning loop.
Don’t ignore feedback from your own team. Real-world review beats theory every time.
How our fraud methodology stands apart
We pit our fraud analytics against old-school tools. Here’s where our approach outpaces static systems, especially when risk keeps changing.
Ravinelora
Self-updating analytics, live risk, transparent alerts
4.7/5
Moderate
Detection method
Legacy tools rely mostly on fixed rules that rarely change, missing new types of risk as fraud evolves. Our analytics refine themselves as the data shifts, learning from each case flagged.
Self-improving
Alert system
Most platforms trigger alerts on preset thresholds—often too late or too broad. Our process sends context-rich signals in real time, tuned to business needs.
Real-time
Reporting depth
Dashboards in standard systems show the basics. We add drill-downs, trend tracking, and custom filters, so teams find issues faster and adapt on the fly.
Granular
Integration speed
Traditional fraud prevention can take weeks to fit new platforms. We map and connect your data with minimal disruption—often live within days.
Rapid setup
Local adaptation
Legacy systems struggle with regional nuances or unique business models. Our tools let you set custom risk rules, adapting to local patterns and edge cases.
Fully tailored
Transparency
With legacy tools, it's hard to see why a transaction is flagged. Our analytics show the 'why' behind every alert, so teams can trust and refine decisions.
Clear rationale
Adaptable, context-driven, always iterating
Legacy fraud tools
Static rules, limited learning, slower updates
2/5
High
Detection method
Legacy tools rely mostly on fixed rules that rarely change, missing new types of risk as fraud evolves. Our analytics refine themselves as the data shifts, learning from each case flagged.
Static checks
Alert system
Most platforms trigger alerts on preset thresholds—often too late or too broad. Our process sends context-rich signals in real time, tuned to business needs.
Manual alerts
Reporting depth
Dashboards in standard systems show the basics. We add drill-downs, trend tracking, and custom filters, so teams find issues faster and adapt on the fly.
Generic dashboard
Integration speed
Traditional fraud prevention can take weeks to fit new platforms. We map and connect your data with minimal disruption—often live within days.
Slow onboarding
Local adaptation
Legacy systems struggle with regional nuances or unique business models. Our tools let you set custom risk rules, adapting to local patterns and edge cases.
Limited flexibility
Transparency
With legacy tools, it's hard to see why a transaction is flagged. Our analytics show the 'why' behind every alert, so teams can trust and refine decisions.
Opaque logic
One-size-fits-all, slow to adapt