One-third of all inter-carrier calls to wireless and wireline subscribers are now considered to be high risk (scam/fraudulent) or nuisance, according to TNS’ 2018 Robocall Investigation Report. Carriers recognize the havoc robocalls are causing on the subscriber experience, and are working alongside regulators, legislators and industry to regain the upper hand from nefarious robocallers.
The FCC-sponsored call detection framework STIR/SHAKEN will provide a key foundational layer in attacking bad actor robocalls. STIR/SHAKEN deployment will mitigate the use of call spoofing because calls will have to be authenticated via the framework. In other words, spoofers will need to now use real numbers to have calls get through the carrier networks that have adopted the protocol.
For carriers, STIR/SHAKEN should be part of a multi-layered approach that addresses short-term and longer-term vulnerabilities that bad actor robocallers will seek to exploit. A comprehensive approach should not only incorporate strategies to reduce the impact of bad actor robocalls, but also seek to identify solutions that enhance the business and consumer subscriber experience.
One service provider objective should be to help business subscribers protect their brand from the damage nefarious robocalls— particularly spoofing—can cause, while also addressing the fact that businesses conducting legitimate calling campaigns are seeing answer rates decline. On the consumer side, carriers know that if they can’t get a handle on the unsustainable volume of bad actor robocalls, subscribers’ frustration can lead to increased churn, reduced usage and eroding trust in voice calling.
Below are three strategies to consider:
Recognize this isn’t just a Tier 1 carrier problem
The implementation of STIR/SHAKEN will move forward at varying speeds, initially across the major tier one phone carriers. During this period of time in 2019, robocallers may shift their attention to tier two and tier three carriers where numbers are not yet authenticated by the framework. This means that carrier, policy and industry stakeholders must resist the temptation to focus narrowly on the progress of Tier 1 carriers (which account for only 41% of all US voice cross-carrier traffic), and look more broadly at how robocallers will seek to identify vulnerabilities throughout the entire telecom infrastructure.
Advanced machine learning methods for blocking negatively scored robocalls using real-time artificial intelligence in combination with big data gleaned from the network can help tier two and tier three carriers prepare for and manage any potential spike in high risk and nuisance robocall traffic.
Restore trust in voice calling
Often lost in the broader discussion about scamming, spoofing and spamming of consumers is the devastating impact robocalls have on legitimate businesses that rely heavily on robocalls. Call center-centric businesses, like financial institutions alerting their customers to potential fraud or pharmacies trying to let customers know their prescription is ready, are just a few examples of legitimate businesses that can see their viability threatened.
Based on technology innovation underway, carriers are increasingly eyeing branded and trusted calling solutions as a way to address business customer frustration with robocalls. At its most basic level, branded calling is when legitimate robocalling businesses place their logo on the screen of the call recipient. For example, a Walgreens logo that appears on your phone when the pharmacy calls to say your prescription is ready. Branded calling not only provides this helpful identification on the incoming call screen but allows the brand to provide additional information – pharmacy location, hours of operation, nearby doctors offices, etc.
Branded calling enriches the relationship between business and customer through better interaction and engagement, but it doesn’t guarantee the brand logo a user sees on his/her phone corresponds to the call originator. The evolution from branded to trusted calling requires solutions that allow businesses to verify their legitimate campaigns so they are whitelisted by carriers. This is an opportunity for mobile operators to help preserve and if necessary rebuild trust in voice calling.
Use data to spot evolving robocaller tactics
As referenced earlier, implementation of STIR/SHAKEN is a core but not singular solution to nefarious robocalls. Spoofers could still, for example, use VoIP for subscription fraud—calls that look authenticated to the user because they are real numbers. In reality, these “real” numbers have been temporarily hijacked by robocallers, who can rapidly churn through authenticated numbers, get caught, dump the numbers, and move on to a new set of numbers.
Data plays a critical role in helping carriers and industry partners protect subscribers. Advanced machine learning methods for blocking robocalls using real-time AI in combination with big data gleaned from the network addresses the constantly changing identities of robocallers. This methodology makes it possible to create an algorithm which can detect call patterns without requiring crowdsourced reporting. As an additional input to this model, crowdsourced feedback allows the analytics provider to layer in context.
Supplementing the unstructured data provided by the machine learning methods, crowdsourced data allows the analytics layer to provide information at a more granular level, such as whether a telephone number is being used to offer free cruises, or is a legitimate call from a bank with a fraud alert related to a credit card.
