May 24, 2023

How Do Carrier Analytics Engines Label Scam Calls?

How Do Carrier Analytics Engines Label Scam Calls?

STIR/SHAKEN gives carriers ways to protect consumers and businesses from robocalls. However, the technology has some loopholes that make it less than ideal. Until regulators and industry leaders can close those loopholes, carriers will continue to rely on analytics engines (AEs) to help them identify scam calls before they reach consumers.

Not sure what a carrier analytics engine is? The following explains how some of the most effective AEs work.

How Do Carrier Analytics Engines Work?

Carrier analytics engines use several strategies to identify and combat robocalls. Some of the most useful approaches rely on machine learning, behavioral analytics, and audio fingerprinting.

Each of these options offers a unique approach to spotting and fighting scams. Some can even work together to pinpoint suspicious activity.

Machine Learning Analytics Engines

Machine learning makes it possible for algorithms to review massive amounts of information to identify suspicious patterns. Machine learning analytics engines look at metadata such as:

  • Call origination
  • Call destination
  • Media type (SMS, audio, etc.)
  • Call duration
  • Call connection status

The industry has more than enough metadata to accurately identify robocall patterns. Perhaps even more importantly, machine learning algorithms evolve over time. Scammers and spammers will inevitably look for new ways to avoid detection. Machine learning AEs pay attention to emerging trends to identify those new strategies as quickly as possible.

Call Behavioral Analytics

Analytics engines often look for behaviors associated with spam and scam calls. For example, a behavioral analytics engine might look for:

  • High call frequency only possible with a robodialer
  • A high volume of calls placed on a single number
  • A lot of calls that take place at odd times, such as early in the morning or late at night

These and other types of information are saved in carrier and third-party databases. Analytics engines can then review the stored data and apply labels to activities that appear similar to those used by scammers.

Audio Fingerprinting

Audio fingerprinting is a relatively new and interesting approach to identifying scam-likely calls. Audio fingerprinting technology involves detecting speech, examining the speech content, adding the information to a database, and using algorithms to determine whether the speech conforms to patterns associated with spam and scam calls.

Audio fingerprinting algorithms make decisions based on user feedback and machine learning. This combination of technologies allows companies like Robokiller to compare call content and find call scripts.

Obviously, many legitimate companies use call scripts to help agents connect with customers, leads, and prospects. However, robocallers often use prerecorded scripts that sound exactly the same each time. Even when a scammer uses hundreds of phone numbers to evade detection, audio fingerprinting quickly recognizes the script and labels calls to protect consumers.

Similarly, audio fingerprinting helps prevent number and brand spoofing by focusing on call content instead of just phone numbers.

Carrier Partnership With Analytics Engines

Major carriers in the U.S. usually partner with third-party companies that develop carrier analytics engines. For example:

  • AT&T uses Call Protect by Hiya
  • Verizon uses Call Filter by TNS
  • T-Mobile uses ScamShield by First Orion

Since carriers use different AEs, labels can differ from company to company. The varying algorithms and labels also make it important for you to use call monitoring that works across all major carriers.

Mitigating Call Labels for Legitimate Businesses

Carrier analytics engines are some of the most effective tools in the fight against robocalls. Algorithms don’t work perfectly, though. There’s always a chance that they will apply negative call labels to numbers used by legitimate businesses. That means you should take a proactive approach to monitoring your number reputations and remediating flagged numbers.

Protect your numbers from inaccurate flags by:

  • Scanning newly purchased numbers before you use them.
  • Following ethical dialing practices like using legitimate lead lists and training agents to use empathy.
  • Follow federal and industry compliance standards.
  • Verify the businesses you partner with to make sure they follow ethical practices.
  • Monitor your numbers across all carriers to catch labels, flags, and blocks as soon as possible.
  • Redress inaccurate labels immediately, so you can put affected numbers back in rotation.
  • Remediate any practices that might contribute to getting flagged in the future.

Stay Ahead of Harmful Labels

Caller ID Reputation has a suite of products developed to keep your phone numbers’ reputations pristine. Phone number monitoring shows you when your numbers get flagged, so you can address the situation immediately. Actual device testing uses real devices to show you what caller ID screens show when you call them. You can even get help with number redress remediation to get your numbers back in rotation as soon as possible.

Sign up for a five-day free trial to learn more about how Caller ID Reputation benefits call centers and other organizations that make a lot of outbound calls.