Prevent fraud with machine learning and artificial intelligence.
Fraud and identity theft are constantly evolving – today’s fraud cannot be stopped with business rules and traditional methods alone. RevHub Fraud Firewall identifies fraudulent claims and identity theft before they are processed and stays ahead of the latest schemes with artificial intelligence and machine learning (AI/ML). Designed to be adaptable and scalable to client needs, leadership mandates, and internal security requirements, Fraud Firewall empowers your investigators, reduces processing time, and accelerates revenue collection.
Contact Us to Schedule a Risk-Free Assessment
Contact us to schedule a risk-free assessment that uncovers fraud and identity theft in your existing systems. Our expert team uses artificial intelligence and machine learning to analyze data, detect fraudulent activity, identify false positives and define strategies for remediation and prevention. In less than 60 days, our team will
- Identify your existing blind spots and fraud vulnerabilities
- Produce an actionable, tailored plan for future prevention, and
- Demonstrate how much revenue your agency could potentially recoup.
RevHub Fraud Firewall
RevHub Fraud Firewall proactively identifies and prioritizes fraud risks before they are exploited. Machine learning algorithms detect the latest schemes and Intuitive reports ensure that insights are understandable and actionable.
With multiple AI/ML analytical techniques for fraud detection, Fraud Firewall has prevented more than $1 billion in fraud since its inception.
Real-time scoring allows claims and applications to be evaluated as they are submitted, resulting in automated identification of fraud and identity theft.
RevHub’s Case Management module unifies data and automates processes to maximize agent productivity, streamline workflows, and reduce customer wait times.
User-friendly reporting makes it easy to track meaningful KPIs including fraud dollars stopped, ROI, average processing time, hours of labor saved, and more.
