Fighting SMS fraud with real-time AI

Over 45 million SMS messages are sent every day in Norway. Among them, thousands are fraudulent—attempts to deceive or defraud users via fake links or impersonation. For telecom operators, the challenge is urgent: stop the bad ones, but never block a legitimate message. In collaboration with a leading telecom provider, Antire developed an AI solution that filters fraudulent SMS messages in real-time with high accuracy and zero disruption to end-users.

Challenge: Daily, over 45 million text messages (SMSes) are sent in Norway. Many are sent from computers, and large amounts of these are attempts at fraud. Almost everyone has experienced receiving a fraudulent SMS. In a recent survey, over 70% of respondents reported having been defrauded. At the same time, there is zero tolerance for legitimate messages to be stopped. This creates a demanding need for precise filtering to distinguish between fraudulent and legitimate incoming SMS messages, primarily because the selection of fraudulent SMS messages must occur with virtually no delay.

Solution: We developed a machine learning solution that analyzes and filters SMS traffic in real time, based on more than 100 million log lines daily. The model identifies suspicious messages with high precision and continuously adapts to new fraud patterns. At the same time, the model ensures that no legitimate SMS is stopped.

Result: Our solution was implemented and delivered excellent results for the customer. The telecoms company nevertheless decided to replace the solution with commercial off-the-shelf products to increase IT standardization within the company, even though the solution cost more than six times that of Antire's solution. 

However, the change yielded significantly worse results, along with the high price, that Antire's AI solution was implemented again. This results in significantly fewer fraudulent SMS messages for the telecom company's customers as well as a lower error rate. The result is far greater customer satisfaction. The system protects mobile users against fraud, without stopping genuine messages.

Antire has completed several hundred AI and machine learning projects for our customers. What the projects have in common is that they can be developed and implemented in a short time, while providing outstanding value to customers. Here you can read about four of the projects.

Antire has been working with machine learning and artificial intelligence (AI) since 2013. Over the years, we have completed several hundred projects across various industries and with some of the leading enterprises in the Nordic region. 

What most of the projects have in common is that they involve few people and take a short time. It usually takes 1–2 months from start to implementation and production. The projects typically last no more than six months. The AI solutions solve specific tasks, providing excellent value and paying off quickly. 

Machine learning and AI have followed Moore's Law in terms of development, with capacity doubling approximately every 18 months. In the last couple of years, however, growth has accelerated, with capacity doubling every 3–4 months. This means that the AI ​​solutions we deliver do more and have greater value for customers.

The projects can be made available in several ways, for example, as independent apps, dashboards, and solutions that are integrated into the customers' ERP systems. The value depends on use. It is therefore essential that they have a good interface and are easily accessible to everyone who will use them. If customers wish, they can add their factors afterwards. 

We present four concrete AI and machine learning projects that we have recently developed and implemented:

• Predicting power market imbalances with AI.
• Halving power consumption in mobile towers with AI.
• Where to produce hydrogen and wind? AI finds the optimal locations.

If you’re exploring how AI can safeguard your customers in real-time, let’s connect.

Get in contact with Øyvind Spørck—our Head of Tailored AI and a leading expert in applied machine learning.