The Featurespace Merchant Acquiring Fraud solution monitors merchant onboarding and transactional behavior to proactively identify anomalous chargeback and bust-out activity.
Merchant acquirer fraud is complex, ranging from chargeback fraud to merchant-cardholder collusion, and it exposes acquirers to the liability of transaction level fraud, chargebacks, fines, reputational damage and regulatory sanctions.
Historically, legacy systems have undertaken merchant acquiring and transaction monitoring at a summary level. The Featurespace Platform, instead, analyzes and risk scores individual transactions in real time — stopping more fraud with fewer alerts. The result is less negative impact to your genuine merchants and higher payment conversion with reduced risk.
Reduce fraud losses: Identify suspicious transactional and chargeback activity.
Proactively monitor high-risk merchants: Improved protection against merchant risk by performing real-time analysis on transactions, benchmarking these against existing credit limits and performing peer group analysis to look for merchants acting outside the norm.
Accurately and automatically spot new fraud attacks:
Understanding what normal activity looks like and monitoring all entities in real time enables acquirers to spot anomalies such as BIN attacks or merchant bust-outs.
Improve fraud detection and chargeback performance while simultaneously increasing acceptance.
Zero model degradation:
Self-learning models automatically adapt, learning from investigators’ actions, proactively enabling acquirers to discover new fraud attacks.
The Featurespace Platform is deployed by some of the world’s biggest processors and acquirers to mitigate and reduce fraud losses.
Multi-tenancy enables businesses to service everyone from small customers to a large enterprise.
Easier onboarding of clients
Multi-tenancy enables easy rollout of updates from a central application, instead of requiring individual environment updates.
Analytics configuration groups
With multi-tenancy you can provide specific analytics for a group of tenants while maintaining an underlying service which remains constant for all tenants.
Segregation of data
Segregate data according to business and security rules - ensure that tenants do not see the data of other tenants.
Low-risk portfolio case study: A global payments processor
This global payments processor is among the largest credit card processors in the US and one of the top acquirers in the European marketplace. The organization supports the payment needs of over one million merchant locations, processing over three billion transactions annually.
Challenge
The global payments processor wanted to protect its merchants from fraud—and protect itself from fraudulent merchants. The processor challenged Featurespace to provide a solution that would:
- Detect 50% of fraudulent transactions by volume
- Offer comparative model thresholds: 25% and 75% of fraudulent transactions by volume
- Leverage the latest in machine-learning technology
High-risk portfolio case study: A European payments processor
This European payments processor is one of the fastest growing global acquiring networks. Providing their customers with a secure, international payment processing platform, they wanted a real time, proactive risk management system that could also reduce the number of alerts flagged for manual review while reducing false positives and catching more fraud.
Challenge
A Global Payments Processor wanted to protect its merchants from fraud and protect itself from fraudulent merchants. The processor challenged Featurespace to provide a solution that would:
- Detect 50% of fraudulent transactions by volume
- Offer comparative model thresholds: 25% and 75% of fraudulent transactions by volume
- Leverage the latest in machine learning technology
Curious about The Featurespace Platform and what it can do for your business? Let us walk you through our capabilities, strategies and solutions.