| Yeah, those guys are great. I run into them at security conferences all the time. Their tech is mainly aimed at the financial sector to make sure the person calling in is who they say they are. In our case, we don't/can't analyze the audio stream of consumer calls. We can only work with the meta data of the call. Our algo comes down pretty hard and fast on calls that come into our honeypot. These numbers have been out of service for years and no one should be calling them. Since these phone lines belong to us, we can do anything we want with them - there's no privacy concerns like there would be with consumer calls. We answer, record, transcribe, analyze, and classify almost 10k calls per hour coming into our honeypot. Check out https://www.nomorobo.com/lookup. There's been a ton of health insurance scams running this week but the scam du jour changes every day. When we analyze the call data coming into consumer lines, we have to be a little less aggressive so that we don't accidentally block schools, police, doctors, pharmacies. The only thing left for humans is to help correct the decisions the algo makes. Sometimes it misses robocalls and sometimes it stops things it shouldn't. People just report them through our app and website. For historic analysis, we also ingest the FTC & FCC robocall complaint data sources. But it's not really a great data source for the real-time detection algo - It's just too slow to be actionable. You really have to be detecting robocalls at the moment they come in to have a successful blocking product. |