July 12, 2023
Applying Behavioral Analytics to Combat Spam Calls
As companies find better ways to block spam calls, robocallers find creative ways to circumvent those methods. It’s a cat-and-mouse game that keeps consumers in jeopardy. However, recent improvements in behavioral analytics could give carriers and third-party apps a more effective way to stop spam calls before they reach consumers.
What Are Behavioral Analytics?
Behavioral analytics uses big data analytics and artificial intelligence to find trends in people’s behavior. Many industries use behavioral analytics to target consumers with persuasive messages and identify trends as they develop.
The telecommunications industry uses behavioral analytics a bit differently. Companies can spot activities associated with spam calls by analyzing massive amounts of data generated by callers. For example, a phone number that places ten calls within a minute probably relies on robocalling software. Carriers can use this information to block calls coming from the number. Alternatively, carriers and apps can add labels that warn consumers of potential spam.
In this case, a behavior – placing too many calls within a short period – becomes a trait carriers can use to identify spam calls and take action.
Combining Behavioral Analytics and Machine Learning
Today’s behavioral analytics can consider factors much more complex than how many calls a number places within a certain amount of time. Machine learning can sift through massive datasets to find connections invisible to human intelligence.
As robocallers adjust their strategies to evade detection, machine learning algorithms can recognize the new behaviors. Imagine that a spam caller buys more phone numbers to spread out calls so they won’t get noticed. A combination of machine learning and behavioral analytics could quickly notice the change and fine-tune its strategy to block calls with the newer trait.
Analyzing Behavior to Stop Spam Calls
Behavioral analytics adds an essential new tool that helps carriers stop spam callers. Previous technologies have relied on applying labels and blocks to numbers that match existing patterns. Machine learning and behavior analytics make it possible to stop spammers before they annoy – or defraud – consumers.
Analyzing Call Behavior
Analyzing call behavior makes it possible for algorithms to block numbers that look suspicious because of behaviors like placing an uncommonly large amount of calls or placing calls outside of acceptable hours.
Behavioral analytics engines that learn in real time can identify these traits and stop them quickly. Without fast data processing, a carrier might need to wait days or weeks before it could spot a bad actor. Now, it can potentially recognize spam callers within minutes or hours.
Analyzing Audio Fingerprints
Audio fingerprints further expand the power of behavioral analytics. With audio printing, carriers, and third-party apps don’t need to know a number and its behaviors to determine whether it presents a problem.
Audio fingerprinting algorithms analyze the content of calls. For example, if an algorithm find that a spammer uses a recorded script to trick consumers, they can label or block calls regardless of what numbers the spammer dials from. If the algorithm knows that “Script A” comes from a spammer, it can review content from new calls coming from unknown numbers to find matches. If those unknown numbers have content that matches “Script A,” the calls get blocked or labeled.
Audio fingerprints use algorithms similar to those created by music-recognition apps like Shazam. Shazam records a snippet of music and compares it to a music library. It can tell you a song’s name when it finds a match. Telecommunications companies use a version of this technology to match current call content to known spam, making it easier than ever to stop criminals before they can reach consumers.
Analyzing Data Leaks
Data leaks can give scammers the contact information they need to reach unsuspecting consumers. Luckily, the number of individuals affected by data leaks has decreased significantly in recent years, even as the number of leaks increased. In 2018, there were about 1,175 data compromises in the U.S. They affected more than 2.2 billion people. In 2022, there were about 1,800 compromises, but they only affected about 422 million people.
Behavioral analytics algorithms can use data leaks to protect consumers. As the algorithms learn where scammers get leaked phone numbers, they can pay attention to those websites and collect the numbers that get leaked.
Knowing which phone numbers have been exposed through recent data leaks adds another layer to behavioral analytics. If someone starts making frequent calls to those leaked numbers, behavioral analytics engines can notice that behavior and determine whether to block or label the calls.
Maintaining Your Phone Number Reputation
Outbound dialing relies on accurate caller ID information that helps consumers decide whether they want to answer calls. Behavioral analytics can potentially improve the accuracy of information and protect people from spammers and scammers.
Behavioral analytics could also erroneously identify legitimate businesses as sources of spam. The chance of getting flagged increases when callers use improper dialing habits or annoy contacts. Following call management best practices should help you avoid getting flagged. Even analytics engines make mistakes, so your numbers could get labeled or blocked.
Caller ID Reputation’s Device Cloud service lets you monitor what appears on caller ID displays. Device Cloud gives you screenshots from real devices connected to major carriers, including AT&T, T-Mobile, Ting, and Verizon. If you see inaccurate information, you can take the right steps to remediate the issue.
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