The Federal Communications Commission (FCC) voted in June 2019 to allow wireless carriers to automatically block unwanted robocalls for all subscribers – hoping that a shift from opt-in requirements would reduce the volume of incoming unwanted calls.
Addressing the rule approval, FCC Chairman Ajit Pai stated: “If there is one thing in our country today that unites Republicans and Democrats, liberals and conservatives, socialists and libertarians, vegetarians and carnivores, Ohio State and Michigan fans, it is that they are sick and tired of being bombarded by unwanted robocalls.”
The FCC Chairman joins policymakers, carriers and industry stakeholders in taking more aggressive action on robocalls. While automatic call blocking may seem straightforward in policy and execution, there is a reason robocallers have been so difficult to reign in: they rapidly adjust tools, tactics and scams, which makes it difficult to discern unwanted from wanted calls. These challenges help explain why only 39% of wireless subscribers* want their carrier to automatically block all calls from numbers not in their mobile phone contact list.
For automatic call blocking to work as intended, there are several factors and strategies that carriers should consider:
Recognize robocalls are not created equal
Consumers are increasingly frustrated with the onslaught of robocalls. To understand the scale of the problem, TNS is identifying over 200 million negative calls crossing its carrier networks every day. But, all robocalls are not created equal in the minds – and ears – of consumers.
Individuals reported losing $1.48 billion to fraud last year – an increase of 38% over 2017, and it isn’t just older individuals being victimized. In fact, younger people reported losing money to fraud more often than older people. Let that sink in. It’s what the data has been revealing for a while, but it’s hard for people to grasp. Last year, the reported loss due to fraud amongst people in their 20s was almost three times (43%) as great than for people in their 70s (15%). When people in their 70s did lose money, the amount tended to be higher: their median loss was $751, compared to $400 for people in their 20s.
As referenced, less than four in 10 (39%) wireless subscribers want their carrier or mobile device manufacturer to automatically block all calls from numbers not in their mobile phone contact list, primarily because they would have no knowledge a caller had tried to contact them.
However, consumers are much more amenable to have their wireless carrier automatically block calls when those calls are deemed high-risk (scam/fraud). Almost eight in 10 (78%) of consumers want their carrier to automatically block these calls, letting others come through to the handset so they can choose whether to answer, send to voicemail or block.
At the same time, a majority of consumers still want to utilize voicemail for call screening. A majority of consumers (69%) want lower-risk calls sent to voicemail, letting them choose which messages they want to return. The takeaway for carriers, policymakers and regulators is that while consumers want protection from robocalls, they still want to retain some control for less damaging nuisance calls.
It’s all about the data analytics
Without trust in the underlying data, it will be impossible for consumers to feel comfortable that ceding control in call blocking is in their best interests. Today, it is already possible to detect caller ID spoofing and other malicious and nuisance robocalling behavior based on real-time network data analytics.
When it comes to automatic call blocking, data analytics and machine learning are critical to determining with speed and accuracy which calls should be blocked in real-time and which ones to let go through to subscriber phones. From our analysis of 1 billion call events per day across more than 500 telecom operators, we can identify robocaller tactics and trends to identify which calls are legitimate. Machine learning can be applied for machines to learn based on the data without being explicitly programmed to do so.
This requires a lot of data inputs into the machine learning, to determine if an incoming call is from a scammer trying to steal personal and financial information, or a “wanted” robocall from, say, your child’s school or the pharmacy notifying you a prescription is ready. Combining machine learning for accurate call filtering/blocking decisions and human analytics is necessary for effective automatic call blocking. Carriers must continue to employ trusted solutions to ensure the right automated call control decisions are made.
Prioritize consumer education
Subscriber support for automatic call blocking will require a better understanding of how it would work and what type of control consumers will retain. Subscribers will want to be confident that important robocalls from their doctor’s office, bank’s fraud department or kids’ schools will not be blocked by default, and that unwanted calls will not get through.
For carriers, this means clear and consistent communication to their subscriber base on which actions can be taken if certain calls aren’t being handled properly (such as adding trusted numbers to their contact list) and options on tools to block calls from any number that doesn’t appear on their customer’s contact list or other “white list.”
Subscriber education should also extend to these robocall detection tools as well. More than 70% of consumers surveyed agree that they would like to use an app from their wireless carrier to identify potential robocalls, however, they are not aware that such an app is offered. This suggests a need for more aggressive consumer education regarding the availability of this service/technology and the benefits these apps can provide.
STIR/SHAKEN is a foundational layer, not a silver bullet
Carriers – as well as handset manufacturers – must continue to think about how various types of calls are displayed on the subscriber device based on STIR/SHAKEN once the framework is fully deployed.
Some recent media coverage of Apple adding STIR/SHAKEN support to iOS 13 suggests that the feature will be of limited value. This is due to the fact that iOS 13 users would only find out if a call is verified by scrolling through their call logs to see a checkmark icon on verified calls that already came through, rather than a real-time “Caller Verified” display for incoming calls.
True, there is more onus on consumers to go through call logs after-the-fact. However, a user study TNS conducted earlier this year finds that even real-time call verification display may not be enough to change consumer behavior. For incoming calls from an unknown number, including a ‘Telephone Number (TN) Validation Passed’ icon did not lead to materially different call answer/block rates compared to just displaying the number. Eight in 10 people don’t answer a call from an unknown number even with a TN Validation Passed icon, which speaks to a need for further consideration on the type and volume of information to be displayed.
For those quick to judge the effectiveness of the STIR/SHAKEN display, consider that it took Firefox 17 years, 70 versions and 80% of webpages to be secure before they would mark websites as not secure. Similarly, it took Google 11 years and 68 versions. The point is that building consumer confidence in a validation system – whether it’s secure/unsecure websites or validated/unvalidated incoming calls – is not an overnight process.
On the flip side, businesses can fully manage their voice calling brand – businesses and telemarketers have full flexibility to use branded calling to deliver their name, logo, and if desired, the intent of the call, whether that be an appointment reminder or a customer account alert that requires immediate attention.
Automatic call blocking is part of a broader and necessary effort to more aggressively combat robocalls and shift much of the burden and associated frustration away from subscribers. For the FCC rule to be implemented effectively by carriers, it is important to keep these factors in mind.
*KANTAR commissioned a survey on behalf of Transaction Network Services. The survey interviewed 1,017 US adults and was conducted by online self-completion interviews between June 27 – July 1, 2019 by Kantar. The survey is designed to be nationally representative of adults interviewed per country. The surveys use a quota sample based on age interlocked within gender and a regional quota. Post fieldwork correctional weighting within age, gender and region has been used to ensure the representativeness of the survey.
