Combating AIT Fraud with AI: Strategies for Success
The telecom industry is projected to reach $4.16 trillion, with a Compounded Annual Growth Rate (CAGR) of 6.35% (1), in a significantly mature marketplace. A recent survey of telecom losses puts the figure at a whopping 2.2% of the current market size of $1.8 trillion. An increase of 28% from 2019. Among telecom frauds, AIT fraud has been on the rise since many Mobile Network Operators (MNOs) have not invested sufficiently in preventing Artificially Inflated Traffic (AIT) and Artificially Generated Traffic (AGT). AIT (which includes AGT) results in tremendous losses to both operators, and according to Forbes, this figure has grown 600% in the past 10 years to $10.76 billion (2).
So, who gets affected? Both user and operator. So, who is accountable? Also, both user and operator. So, how do we prevent it?
What is AIT fraud? What constitutes AIT fraud, how it works, and what effect it has?
Before we look at ways to prevent it, let us first look at what AIT is and the different types and causes of AIT fraud. AIT usually begins with the name itself – Artificially Inflated Traffic. The fraud perpetrators start by triggering avoidable and often unnecessary calls, SMS text messages, One-Time-Passwords (OTP), and other MNO services that either cost the customer or the operator.
How it works is that when the user’s mobile phone makes calls, sends a message, or triggers the receipt of a message, it triggers the accounting workflows between the user’s MNO and the target MNO. The target MNO in this case, is usually one with high termination fees. Since termination fees are revenue streams for MNOs, the fraud perpetrators can benefit from a revenue-sharing agreement with the target MNO who may be completely unaware of the unscrupulous nature of the traffic generated.
AIT often escalates into other types of fraud, including hacking, phishing scams, and identity theft, where the victim’s mobile phone can be used to create Artificially Inflated Traffic in the form of calls to high-termination MNOs, but also result in fake numbers, and a multitude of possibilities that perpetrators can exploit with ease. Another significant source of AIT fraud is when the Caller Line Identification (CLI) is spoofed to prevent the receiving MNO from identifying the source of the call, thus taking unfair advantage of the difference in termination rates.
The losses to operators, compounded with churn, regulatory intervention, significant loss of credibility, etc., have serious repercussions which require immediate action.
Detection& Prevention of AIT fraud
Having delved into how AIT fraud is perpetrated, there are ways to detect, reduce, mitigate, and prevent AIT fraud, but these are not simple. Preventing and reducing AIT fraud requires the use of the right set of tools and technologies, along with a consistent and conscious approach from both the operator and the user. Let us look at some of the ways to combat this fraud:
Analytics and AI: Analyzing traffic, identifying patterns, and automating responses based on historical data are crucial to ensuring a progressively increasing number of AIT frauds are eliminated before it enters the network. Some of these methods include:
- Pattern Detection: AI can analyze traffic patterns and detect anomalies in data much faster and more accurately than humans. By using machine learning algorithms, AI can detect traffic patterns that are artificially inflated and alert authorities.
- Real-Time Monitoring: AI can monitor traffic in real time and detect any suspicious activities. This can help prevent artificially inflated traffic fraud before it happens.
- Analytics: AI can use predictive analytics to detect traffic patterns that may indicate artificially inflated traffic. By analyzing historical data, AI can predict when a website may experience artificially inflated traffic and take action to prevent it.
- Behavioral Analysis: AI can analyze user behavior to detect whether the traffic is real or artificially inflated. By analyzing factors such as user location, browsing history, and click patterns, AI can identify whether the traffic is from real users or bots.
Monitoring: Whether at the operator level or the user, monitoring of calls, messages, and other network activities at the network level and the user’s device is crucial to identify potential AIT fraud instances. From simply blocking typical calls and messages originating from fake numbers to reporting spam, regular anti-malware scans can help users prevent losses to themselves as well as the network operator. Likewise, operators can use network scanning and monitoring tools to identify the AIT and devise approaches to mitigate AIT fraud.
Vigilance: While the technology component – firewalls, monitoring tools, and AI can significantly improve the efficiency of AIT fraud reduction, the users also have to assume responsibility for the traffic that they experience on their devices. Refraining from installing apps without verifying their authenticity, and avoiding circumventing content restrictions, as these are significantly large sources of AIT in the form of spam calls and text messages, and fraudulent apps, which may, in turn, result in spam, as well as OTP fraud, phishing, and other potential causes for losses both for the user and the operator.
As much as our technology solutions improve in their ability to detect and prevent AIT fraud, they cannot all be eliminated in one fell swoop. And until it becomes untenable for the perpetrators, it is up to users and operators alike to remain cautious and vigilant and keep updating their understanding of how, why, and what perpetrators of AIT do to achieve their goals.
Explore the Use Cases and Strategies for Combating Frauds with Advanced Analytics and Machine Learning
Harish works with Subex as a Technical Product Manager for Fraud management and is also a part of the business consulting group as an SME for a signaling security solution. He has more than 7 years of experience in the space of fraud and risk. He has abundant experience working with telcos across the globe on implementing signaling solutions and VAS products.
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