Not everyone on the other end of a phone conversation is who they say they are. Those bad actors are not only a risk, but you waste valuable time, money, and resources handling them. Our technology flags all forms of ANI spoofing — providing threat-level analysis in milliseconds — to verify identity and validate phone numbers. Our insights allow you to keep your good customers close, and keep the bad actors out.
Fraud Solution Profile
Next Caller’s phone fraud prevention technology is a real-time API that integrates with any telephony system to review every incoming phone call for signs of ANI spoofing. Using actual call meta-data, Next Caller’s API produces a fraud threat-level reading for every incoming call. Our technology can detect all forms of ANI Spoofing, and indicates when the spoof is thought to have malicious intent.
Once the fraud threat level is produced, the reading is transmitted back to the business in milliseconds, via the same API connection. Based on predetermined rules, the business can take instantaneous action to improve the call experience for good customers (green-light, no hassle), mitigate risk (re-routing suspicious calls to an enhanced screening process), blocks calls entirely (in the case of robo dialing or traffic pumping).
Our technology ensures that your business will keep good customers close, and keep the bad actors out, adding confidence and clarity to your customer engagements.
Next Caller offers an initial assessment of your existing phone system vulnerabilities and current fraud procedures, then develops a custom program to fit your unique environment. After implementation, our technology is calibrated using real-time performance metrics and a sophisticated feedback loop to address any discovered anomalies. This process reduces, and can eventually eliminate the false positives and false negatives that impact customer experience, and your bottom line.
More information is available upon request.
Ecommerce, Financial Services, Insurance
Identity & Authentication
Call Center Fraud, Payment Fraud