The telecom industry is witnessing an alarming surge in fraudulent activities. The consequences are severe, resulting in a massive worldwide annual loss of approximately $32.7 billion. But financial losses are not the sole concern. The very reputation of telecom providers, painstakingly built over years, suffers much more from telecom fraud.
But why does telecom fraud continue to pose such a grave challenge, with so many technological advancements already existing? The answer lies in the adaptability of the fraudsters themselves. These cunning foxes constantly refine and modify their tactics, trying to be one step ahead of even the most vigilant security measures.
In this regard, artificial intelligence (AI), in particular, machine learning (ML) algorithms able to learn on past fraud data and adapt to similar patterns in the future, is one of the counter-fraud measures that, when used wisely, can give your telecom company an advantage over hackers. The more data you provide to the ML-based system, the more adaptable and accurate it becomes against fraudsters.
At Flyaps, we boast extensive experience in both AI and telecom fraud detection. Industry giants like Yaana Technology have relied on our expertise to enhance their digital security. In this article, we will delve into the crucial role that AI plays in combating the issue of telecom fraud, discuss common types of telecom fraud and explore real-life cases where the use of AI-powered solutions offers a ray of hope for the industry.
Advantages of AI as a modern solution to telecom fraud
Traditionally, telecom fraud detection has relied heavily on human analysts. They identified suspicious activities by using predefined rule-based algorithms, and conducted subsequent investigations manually. While this approach had its merits, it became increasingly ineffective against fraudsters who keep adapting and refining their tactics, and often outwitting human monitors. As a result, fraud in the telecom industry has persisted, leading to substantial financial losses each year.
Introducing AI marks a significant shift in the strategy for telecom fraud prevention. To get the true power of AI in fighting against telecom fraud, it's essential to understand how it surpasses traditional methods:
- Real-time analysis and automatic prevention: AI operates continuously in real-time, analyzing vast data volumes at lightning speed. Unlike manual analysts that review data periodically, this technology remains on guard 24/7, detecting and preventing fraud the moment it occurs, minimizing financial losses.
- AI algorithms' learning capability: AI algorithms are dynamic and adaptable. They can be trained to discern legitimate communication from fraudulent activities, improving accuracy over time and reducing false positives. As fraudsters come up with something new, AI adapts to stay ahead.
- Identification of unusual patterns: Telecom fraud involves complex and ever-changing tactics. Traditional methods struggle to keep up, but AI excels at recognizing unusual patterns and behaviors. It can detect subtle deviations from normal customer activities, such as increased data usage or irregular call patterns, raising red flags before a fraudulent activity fully unfolds.
Another compelling reason to employ AI for telecom fraud detection is the fact that cybercriminals are already leveraging this technology for their activities. For instance, fraudsters create highly convincing phishing text messages, which appear to be from trusted sources, fooling unsuspecting recipients into leaking sensitive information. Sophisticated attackers also may attempt to override or overwhelm telecom AI systems, potentially deceiving it into perpetually targeting themself.
To effectively safeguard your operations, you must stay ahead in this technology race and harness the power of AI for proactive telecom fraud prevention.
Different types of fraud in telecom and how AI responds to them
Bigger telcos usually distribute their services among smaller local networks and providers that often don't have very strong anti-deceptive systems. That makes big players vulnerable to second-hand fraud and complicates security breach prevention.
Generally, fraudsters attack either telecom providers or their customers. In the first case, hackers enter through voicemail systems or improperly discarded SIM cards, making unauthorized calls, often to high-cost destinations. When it comes to customer frauds, it involves creating a covert fraudulent system that targets customer bills, remaining invisible to both the service provider and customers until a substantial bill is generated, often too late for detection.
Let’s explore the most common cases of fraud activities frequently encountered in the telecommunications sector and how AI can help.
Let’s imagine that Sarah, a telecom customer, receives a call from an unfamiliar international number. Her phone rings just once, and the call abruptly ends. Curious about the missed call, Sarah dials the number back, unaware of the potential scam.
AI is at work in the background, analyzing call patterns and detecting that Sarah's response fits the profile of Wangiri fraud. It recognizes that the brief, one-ring call followed by a return call is a hallmark of this scheme.
The AI system promptly alerts the telecom operator, who then investigates the situation. They confirm that the international number is a premium rate line, and thanks to AI's quick detection, Sarah avoids incurring expensive charges. The fraudsters' attempt to trick her into making a premium number call is eliminated
Here’s another case. John, a mobile phone user, receives a flurry of SMS messages claiming to be from various banks, asking for his personal and financial information. These messages request details like his account number, PIN, and Social Security number, along with links to seemingly official websites. All these messages are part of a large-scale SMS phishing operation.
AI-driven security measures are in place to protect John and other users. The AI system monitors SMS traffic for unusual patterns, detecting the mass spamming of messages from various sources and recognizing the repetitive nature of these messages across different recipients. It also scans for known phishing keywords, identifying phrases and terms commonly associated with phishing scams.
The AI system flags the suspicious messages sent to John and other customers, warning them not to interact with these texts. It also alerts the telecom provider, which then blocks or investigates the malicious sources.
SIM jacking and SIM swapping
David, a smartphone user, suddenly finds himself unable to make calls or receive text messages. Concerned, he contacts his telecom provider, who informs him that his phone number has been linked to a new SIM card, which he never authorized. It's a clear case of SIM jacking, and the fraudsters are now in control of his calls and texts.
Fortunately, an AI-driven system is actively protecting David's account. The solution has been monitoring his typical behavior and usage patterns. It recognizes that this SIM card transfer deviates significantly from his usual activities and raises an immediate alert.
David's telecom provider immediately places his account under enhanced security checks. They require him to verify his identity through additional authentication methods before restoring his access. Thanks to AI's vigilance, the telecom provider prevents any further misuse of David's phone number, keeping his personal information and accounts secure.
International revenue sharing fraud (IRSF)
Onto business use cases of AI. M Corp, a global business, notices a significant spike in its telecom bills, with most of the charges linked to international premium rate calls. Their finance department is perplexed because these calls were not part of their regular operations.
AI-based telecom security is employed to analyze call patterns and usage. It quickly recognizes that the recent flurry of international premium rate calls sharply deviates from M Corp's typical customer behavior. These calls were made to obscure premium numbers with excessively high rates, all linked to a single telecom fraud operation.
The AI-based system triggers an alert and sets a threshold for such unusual activity. The telecom provider immediately notifies M Corp, who, with the telecom's help, can block these fraudulent charges and investigate the origin of the calls. The system not only saves the company from ongoing financial losses but also contributes to preventing similar IRSF incidents in the future.
Suppose employees of your telco have been noticing an unusual trend in the company's network. Its call traffic to certain international destinations is significantly higher than usual, yet its revenue from these calls has drastically decreased. It's a clear indication of interconnect bypass fraud.
AI-powered systems are there to protect the network. They track the call routes and immediately detect when calls are being rerouted through unauthorized channels. In this case, they identify a substantial deviation from the norm in call termination rates to specific destinations.
The AI system alerts your telco to investigate the situation. It quickly discovers that fraudsters are using SIM boxes or GSM gateways to redirect calls to lower-cost termination rates, profiting from the rate difference. With AI's assistance, your telco takes immediate action to block these illegitimate channels, ensuring that your network operates securely and cost-effectively.
In a bustling office building, employees suddenly start experiencing issues with their office phone system. Calls to clients are dropped, and there's a noticeable increase in unsolicited spam calls. Additionally, some sensitive customer data is compromised (potentially stolen).
The company has AI-powered security measures in place. These AI systems scan network traffic and quickly detect unusual access and activity on the IP-based PBX system. They spot patterns associated with PBX hacking, including multiple unauthorized login attempts and strange call routing patterns.
The AI system promptly raises alerts, and the IT team immediately takes action to investigate and secure the compromised PBX system. By identifying and responding to the hacking attempt swiftly, the company minimizes disruptions to its business operations and safeguards sensitive customer data.
Suppose, your telecom company suddenly faces a surge in new sign-ups for premium mobile plans and top-of-the-line smartphones. However, something doesn't add up. A deeper analysis reveals that many of these sign-ups are tied to the same identity, yet the locations of registration span different cities and even countries.
The AI-driven fraud detection system at the telco is at work. It analyzes customer profiles and behaviors, identifying these glaring discrepancies in the sign-up locations and patterns. Furthermore, it detects that the same credit card numbers are used for multiple sign-ups, indicating a potential issue of subscription fraud.
The AI system promptly flags these high-risk accounts for further scrutiny by the company's fraud prevention team. With AI's help, your telco prevented further fraudulent sign-ups and protected its resources and reputation.
In a busy online store operated by a leading telecom company, a flurry of transactions catches the attention of the security team. Multiple users are rapidly making small but frequent deposits to purchase prepaid SIM cards and devices. Simultaneously, several new accounts are created from the same IP address.
The AI-powered security system deployed by the telecom operator is on the lookout for such activities. It analyzes online transactions and quickly detects the unusual payment patterns of multiple deposits made in quick succession. Additionally, it identifies the creation of multiple accounts from the same device and IP address.
The AI system promptly raises alerts, and the fraud prevention team takes action. Upon investigation, it becomes evident that these transactions were part of a deposit fraud scheme aimed at generating and controlling IP addresses for further malicious activities.
Real-life cases of AI in telecom fraud detection
In theory, AI-based solutions may appear flawless, but how do they fare in the complex landscape of real-world telecom companies? Let's delve into the practical applications of this technology by examining the experiences of three industry giants and the significant benefits AI has provided them.
AT&T preventing phone scams with AI
AT&T employs AI particularly in combating phone scams. These scams have affected a significant portion of the American population, with reports indicating that one in three Americans has fallen victim to such fraudulent activities. In a 2021 Truecaller study, phone scams amounted to nearly $30 billion in losses over the preceding year, averaging around $500 per victim. To address this challenge, the Federal Communications Commission (FCC) introduced the STIR/SHAKEN framework, which requires carriers to validate caller IDs and the sign-off on calls traveling through interconnected networks.
AT&T employs artificial intelligence to analyze billions of daily calls, looking for patterns that indicate potential robocalls. Suspected robocallers are added to a list, which is then reviewed by human operators to ensure that legitimate automated calls, such as those from school districts, are not mistakenly blocked.
AT&T's AI-powered Network Analytics Solution plays a pivotal role in blocking fraudulent calls, labeling suspicious calls, and is part of its Call Protect service. The company claims to authenticate and verify hundreds of millions of calls daily, both within its wireless network and with other leading US wireless providers.
According to AT&T, it blocked or labeled six billion robocalls in 2020. By mid-2021, they were blocking one billion calls per month. The company's efforts have contributed to at least seven federal enforcement actions against scammers in 2020 and played a role in the FCC's record $225 million fine against Texas-based health insurance telemarketers for their spoofed robocall campaigns.
Deutsche Telekom fortifies network security with AI in response to growing hacker attacks
Deutsche Telekom recognizes that with the increased reliance on cloud services and remote work due to the pandemic, the attack surface for hackers has expanded to include Internet of Things (IoT) devices and wearables.
The company has more than two dozen AI-powered systems to bolster its network defense. These AI solutions continuously monitor the network, identifying and alerting to issues like line disruptions. AI is also utilized to analyze real-time attack patterns, provide immediate countermeasures, and generate reports on security incidents.
The company acknowledges that traditional network security measures are insufficient in the face of evolving threats. They advocate for a shift towards a Secure Access Service Edge (SASE) architecture, which combines network and security in the cloud. SASE simplifies network infrastructure, enhances security, and allows for greater automation. Administrators gain centralized control and oversight, akin to a pilot's cockpit, which ensures efficient and secure network management.
SASE offers not only enhanced security but also improved performance and reduced latency. Deutsche Telekom is also incorporating AI into SASE, enhancing its capabilities. For instance, AI can detect unusual access patterns, such as a work cell phone connecting from an insecure location. Once anomalies are detected, AI takes real-time security actions, such as requesting two-factor authentication or locking the device, to safeguard the network and data.
Vodafone Germany use AI to identify spam numbers
Germany is among the top-20 countries most affected by spam. Vodafone Germany is fighting against the surge in fraudulent calls, which escalated during the pandemic, with an AI-driven initiative called CallProtect. This technology cross-references incoming calls with a database of known spam and scam numbers. If a match is found, users receive an informational message allowing them to reject the call.
The pilot project integrates RealNetworks' spam number database into Vodafone's Secure Net service for Android, which is a mobile network-based security solution. Secure Net, available to Vodafone customers, automatically blocks harmful sites and viruses when connected to the Vodafone mobile network in Germany. Users can activate or deactivate the CallProtect service, which will be provided at no extra cost to existing Vodafone Secure Net users in Germany.
Another AI-driven system used by Vodafone in Europe, including Germany, is the United Performance Management (UPM). UPM handles big data from 11 European countries every day and reduces major network and IT incidents by 70%. It simplifies core operations, allowing staff to focus on critical tasks. UPM will replace over a hundred separate network performance management tools in future and can also identify and prevent fraudulent activities. By 2025, all processes in the network will be automated, including access controls and data protection methods like encryption and anonymization.
How Flyaps can help you improve telecom fraud detection with AI-based solutions
For over a decade, we've been committed to the goal of empowering the telecom sector with cutting-edge technologies, AI included. We're not newcomers, but seasoned veterans who've witnessed the industry's evolution and the dynamic challenges it presents. Working alongside giants like British Telecom, Yaana Technology, and Oracle has given us invaluable experience and a deep understanding of telecom's unique needs.
But our expertise extends far beyond telecom. We've successfully implemented AI solutions in various industries, making us adaptable and versatile no matter the case. Our diverse experience ensures that we can bring fresh perspectives and innovative strategies to your telecom projects.
Ready to embark on a journey that will transform your security and protect your digital future? Reach out to us today.