AI Against Robocalls


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We live in a computer-driven age that is permeating all aspects of our life. This age of technology and innovation has also given us a new type of crime: cybercrime.

Just like the IT and technology being introduced everywhere around us, cybercrimes are reaching us in many of our activities, sadly even affecting our communication on mobile phones.

Today, telecom fraud in particular has become a serious business with an international presence. Telecom fraud is on Interpol’s list of crimes and is addressed by every country through regulators and laws. The funds lost by telecom operators in this battle can reach up to 10% of their revenue. Telecom fraud affects both individuals and companies.

Among the various types of fraudulent calls, the most famous are robocalls. Most of us have been a target of these nuisance calls at least once in our lives. Robocall usage continues to grow at an exponential rate. 

In 2021, robocalls caused $1.62 billion in revenue losses to telcos worldwide. Today, they’re the number one consumer complaint received by the US Federal Communications Commission (FCC).

In the US, mobile subscribers received just under 4.6 billion robocalls in October 2022, marking an 8.9% increase from September on a monthly basis and a 5.4% increase on a daily basis. 

The numbers demonstrate the continued rise of robocalls despite increased enforcement and the roll out of Stir/Shaken. 

Telcos and enterprises have been looking for solutions to combat robocalls for decades. Yet there is no perfect tool. 100% protection against robocalls doesn’t exist today.

Despite the well-known STIR/SHAKEN protocol being accepted as the standard for operators in the US in 2020, it has noticeable shortcomings, leaving operators exposed. For example, a call with A-level attestation still can’t be trusted. The call could have simply been terminated via a SIM box, which provides A-level attestation. Robocalls still get through and false positives continue to result in service denial for clients. Furthermore, STIR/SHAKEN established no means for blocking robocalls, only detecting them.

The FCC seeks further improvements on caller ID authentication and other solutions to prevent robocalls in real time, before they happen. The official document released by the FCC after the last meeting in October 2022 clearly recognized the problem, and they called for comments on how best to address this gap in our caller ID authentication scheme and carry out the TRACED Act’s directive to require voice service providers “to take reasonable measures to implement Second Caller ID Authentication Report and Order.”

Other countries are experiencing the same avalanche of robocalls,and their governments are stepping in with some solutions to improve the situation.  

In India, for example, the telecom regulator is currently drafting a consultation paper supporting a mechanism that would allow phones to display the caller’s name even if the number is not saved on that person’s phone. The idea is to link the data sourced from Know Your Customer (KYC) to this mechanism. Telecom operators are required to collect KYC from users before providing them with a SIM card in this region.

The Australian Communication and Media Authority, in addition to its national initiatives, is closely collaborating with the FCC to combat scam calls.  

Businesses and individuals become increasingly frustrated with the barrage of robocalls and the dangers they present, and they lose faith in their carrier. As a result, telcos face deteriorating customer relations and an eroding brand reputation. Subscription rates drop and revenue losses increase.

Operators also waste  time due to traceback requests and face significant fines.

So, if telcos and initiatives from national regulators have failed to stop this threat for good, what can we do? Fortunately, telecom fraud mitigation technology has evolved leaps and bounds in recent years. Therefore, the telecom players have some tools to choose from, and AI-based solutions are ahead of all of them. 

The Power of Artificial Intelligence 

The global artificial intelligence market was valued at USD 93.5 billion in 2021.

The deep learning segment led the market and accounted for a revenue share of around 37.0% in 2021. This growth is attributed to its complicated data-driven applications, including text/content or speech recognition. Deep learning offers lucrative investment opportunities, as it helps overcome the challenges of high data volumes.

Meanwhile, AI and ML are still getting established as a specific niche of the IT industry. They have already demonstrated excellent results everywhere they were applied, as long as all conditions of correct technical implementation were met.

You may encounter AI in shopping and travel apps, marketing tools, and other similar business solutions that improve our life experiences.

What is striking is that the scope of this technology is massive, and applying it to those areas where we need urgent help might play a crucial role. Moreover, it could be the only tool for successfully fighting crimes such as cybercrimes.

There are excellent examples of fraud management systems based on artificial intelligence and machine learning in telecommunications too. All of them are capable of delivering monumental and, more importantly, immediate results in combating fraud, and it’s surprising that they’re not yet leading the industry. Some of the expert papers confirm that emerging technologies like AI and machine learning are the powerful tools we need to fight telecom fraud successfully. And this is why such solutions are the future. 

Today, most fraud management solutions utilize a standard set of protocols that fail to accurately detect and stop robocalls before they connect:

  • CDR analysis , instead of real-time statistics, is time-consuming and inefficient;
  • Most systems cannot block calls, but only provide functionality to notify; 
  • Outdated methods like threshold-based rules and lists of fraudulent numbers have limited capabilities.

One of the main issues is that most systems can’t stop fraud in real time. They detect attacks after the fact, once the damage has been done. Operators then adjust the settings of their systems in the hope of being better protected against similar future attacks. But the fraudsters evolve, develop new tactics, and a game of cat-and-mouse ensues.

While STIR/SHAKEN was an important milestone, its practical implementation showed that it  fails to attest all calls with 100% accuracy, pushing the telecommunication community and policymakers to look further

The best technological answer to this global challenge might be in employing a system that harnesses the capabilities of artificial intelligence and machine learning. By combining advanced statistics, artificial intelligence, and big data analysis, the telco world is beginning to let out a major sigh of relief. AI and ML technology are still emerging, but it’s certain that their advancement is a step forward. AI and ML tools show higher accuracy and help prevent criminal scenarios.

With the right fraud management system, you can stop robocalls before they strike.

How Does an AI-Based Fraud Management System Work?

Artificial intelligence and machine learning operate on several key principles to offer more accurate robocall detection and real-time blocking.

Deep-Learning Algorithms

An AI engine features self-learning and self-updating algorithms to regularly identify sophisticated patterns of fraud.

Paired with a team of statisticians and engineers regularly reviewing records and manually updating the system, the defense mechanisms remain steps ahead of fraudsters, eliminating the game of cat-and-mouse.

In short, fraudsters can’t fool an AI engine. When regularly trained on a massive amount of data, AI’s deep learning algorithms can detect robocalls on both retail and transit traffic with unmatched levels of accuracy.

Immediate Alerting

Machine learning algorithms allow even well-disguised attacks on hubbing and retail traffic to be detected as they occur. The system ensures automatic robocall detection in real time through established parameters and immediate alerting with granular blocking rules.

With machine learning, robocalls are eliminated before they can do harm, thus preventing direct, operational and reputational losses.

Granular Blocking Rules

Once the parameters of fraudulent traffic are determined by the AI system, the operator is alerted. They can then choose to block the corresponding traffic for a predefined period or allow certain traffic to pass, as determined by their exact needs and preferences.

A major advantage of AI-based anti-fraud systems is the flexibility of their integration. These systems are generally compatible with any voice switch and are capable of dealing with SIP signaling and analyzing SS7 and accounting messages.

An operator is leaving much at risk by not using an AI-based fraud management system.

A Solution That Works

Artificial intelligence serves as a real solution. When trained on a large database, AI systems guarantee telcos, businesses and individuals unprecedented protection against robocalls and all related damage. And the results are measurable.

With nearly 100% detection accuracy and real-time blocking, AI-based systems are protecting subscribers against falling victim to scams. They’re creating trust between transit carriers. They’re giving telcos a chance to repair their reputations, improve customer relations, reduce customer turnover and recover lost revenue.

The results indicate a monumental step forward for operators worldwide. Artificial intelligence is bound to reshape fraud mitigation, impacting telcos, enterprises and the lives of ordinary people.

With unprecedented results, it’s safe to say that artificial intelligence will remain a key piece of any future innovation in telecom fraud management and play a key role in eliminating robocalls for good. Today, service providers are already enjoying the revolutionary benefits of this technology.

Dmitry Sumin
Dmitry Sumin is the Head of Products at the AB Handshake Corporation. He has more than 15 years of experience in international roaming, interconnect and fraud management. Since graduating from Moscow State University, he has worked for both vendors and network operators in the MVNO and telecommunications market.


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