Businesses face financial losses due to fraudulent chargebacks, which are time-consuming to dispute and difficult to prevent with traditional methods. Existing fraud detection systems often lack the sophistication to identify emerging patterns and adapt to evolving fraud tactics, leading to revenue leakage and increased operational costs.

XNOT offers an AI-powered chargeback prevention and recovery platform that leverages machine learning to analyze transaction data and identify potentially fraudulent activities. The system proactively detects suspicious transactions before they result in chargebacks, minimizing financial losses for businesses. XNOT automates the chargeback dispute process by gathering evidence and generating compelling rebuttals, streamlining operations and improving recovery rates. The platform continuously learns from new data to adapt to evolving fraud patterns, providing a dynamic and effective defense against chargeback fraud.

Real-time fraud detection using machine learning algorithms trained on extensive transaction datasets

Automated chargeback dispute process with evidence gathering and rebuttal generation

Customizable risk scoring and rule-based alerts to identify high-risk transactions

Integration with major payment gateways and e-commerce platforms via API

Detailed reporting and analytics dashboards to track chargeback trends and performance

Continuous model retraining to adapt to evolving fraud patterns

XNOT primarily targets e-commerce businesses, online retailers, and subscription-based services that experience a high volume of chargebacks and are seeking to reduce fraud-related losses.