As the fight against fraudsters and spammers evolves, it is important to note that there are varying analytical processes that go into both identifying and eliminating robocalls. In Gerry Christensen’s blog Content-based Analytics Definitively Identifies Fraudulent Robocalls, he notes that in the battle against illegal robocalls, it is important to note that analytics approaches may be defined by two broad categories: (1) Event-based analytics and (2) Content-based analytics. Content-based analytics refers to an in-depth investigation of the phone call content to subjectively explain what has happened. Event-based analytics “relies on network signaling such as Session Initiation Protocol (SIP) messages and other call related data.” In looking at both approaches, it’s important to take note of a few critical differences.
In this article, we cover:
- How analytics engines provide scoring information
- The fight against false-positives
- The accuracy of content-based analytics
How analytics engines provide scoring information
Analytics Engines (AE) that use event-based and content-based analytics offer scoring for enterprises and communication service providers. In turn, the scoring lets them decide what the best course of action is with respect to call treatment—block, label, or redirect—when dealing with robocall numbers that have already gotten algorithmic treatment.
With that said, content-based solutions are the only method that accurately analyzes data that indubitably targets fraudulent robocalls and spammers like call payload (content) as shown below:
The fight against false-positives
Content-based analytics bat nearly 1,000 when it comes to false positives—they rarely peg a call as being fraudulent when it is not actually illegal. On the flip side, event-based analytics are more susceptible to errors as they base their information on indicative rather than definitive data.
Having said that, both content-based and event-based analytics work “within the FCC rules for Reasonable Analytics and Safe Harbor”, according to Christensen. This means that “a number owner/custodian may initiate redress if they believe that a telephone number is being inappropriately treated by a carrier based on AE risk scoring.” Ultimately, redress is solved much quicker in content-based analytics because it has the ability to determine if a particular phone number was involved in a fraudulent or robocall campaign. If that’s not the case, negative feedback should be checked, which might point to callers preferring to not receive certain calls.
The accuracy of content-based analytics
As we continue to fight robocallers and fraudsters, content-based analytics is a highly accurate method for pinpointing scammers. With proof for Telephone Consumer Protection Act violation can be particularly burdensome, it is nice to know that content-based analytics consistently document infractions of the Truth in Caller ID Act. This even includes intent to defraud or cause harm, regardless of spamming.
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