The ever-increasing adoption of digital services and the growing demand for an excellent customer experience make A2P messaging a must for keeping in touch with consumers. This has led to a rise in business messaging traffic volumes. Juniper Research estimates that the market’s value will grow from $48 billion in 2022 to $78 billion by 2027, creating immense revenue opportunities for mobile operators. However, threats such as spam and fraud disrupt A2P monetisation, resulting in subscriber churn, damage to an MNO’s reputation, and direct revenue losses.
SMS firewalls, the bulwark of the operators’ defences, can help mitigate the risks of losing revenues to grey routes and protect subscribers from spam and cybercriminals. Usually a part of an overall monetisation strategy, they enable MNOs to extract the maximum value from the SMS service and also help enforce regulations and prevent malicious SMS usage by unauthorised parties.
However, simply deploying a messaging firewall is not enough, and as fraud constantly evolves, the firewalls require ongoing maintenance and ruleset adjustment to keep up. While this can be done manually, such an approach can’t hold a candle to intelligent solutions employing the latest developments in AI, machine learning (ML) and natural language processing.
Advanced AI capabilities can provide valuable insights into SMS traffic patterns, which can help operators improve their services and better understand their customers’ needs. This data can be used to optimise network performance, improve customer satisfaction, and increase revenue.
AI-enhanced spam detection
AI technology can enhance the effectiveness of the SMS firewall by making it more intelligent and efficient in detecting spam, fraud, or phishing messages. By analysing patterns and trends in message content and metadata, an AI-based SMS firewall can quickly and accurately identify and block malicious messages.
One of the major advantages of using AI for spam detection is its ability to adapt to dynamic threats that evolve over time. Traditional firewalls rely on pre-defined rules and patterns to identify spam, which can quickly become outdated. An AI-based SMS firewall, on the other hand, can learn and adapt to new threats as they emerge, ensuring that the messaging service provider stays one step ahead of the spammers.
Moreover, AI-based SMS firewalls can also analyse the context of messages to determine whether they are genuine. For example, a message that appears to be from a bank but contains spelling errors and grammatical mistakes is likely to be a phishing attempt. An AI-based SMS firewall can quickly identify and prevent such messages from reaching the recipient’s inbox.
Natural language processing (NLP) for Content Analysis
Natural language processing is a field of AI focusing on the interaction between computers and human language. It enables machines to understand, interpret and generate human language.
NLP analyses the content of the messages to identify potential spam or fraud messages. This technique can differentiate between spam and genuine messages that contain similar phrases to avoid false positives. This is particularly important for MNOs as spam messages annoy subscribers and can lead to reputational damage and even legal action.
Typically, NLP is used to process meaning, entities and sentiment to extract patterns which define messages as spam or not. For instance, it can identify certain keywords, phrases, or patterns that are commonly used in spam messages. It can also recognise patterns typical for promotional or transactional messages, which can help to pinpoint A2P traffic disguised as P2P. Another key benefit of integrating NLP into SMS firewalls is reducing the likelihood of false positives while filtering traffic for potentially harmful messages.
ML-powered message classification
ML algorithms use message fingerprinting to improve message classification accuracy. This involves detecting common spam messages, even when the message’s content was modified. For example, suppose a spammer sends a message promoting a fake weight loss product. In that case, the ML algorithm can recognise similar messages promoting other fake products, even if the wording differs.
This approach effectively identifies and blocks spam even at high volumes. It is also ideal for operators as it can be scaled up or down as needed to accommodate changing traffic patterns.
However, the benefits of ML-powered message classification go beyond spam detection. By accurately classifying messages, MNOs can also maximise monetisation opportunities by properly charging businesses for A2P messages. This can be done by implementing tiered pricing plans based on the message type, size, or destination.
Fighting grey routes
SMS grey routes are unauthorised routes used to terminate A2P messages for a margin of their actual price or even completely free of charge, resulting in revenue loss for MNOs. Integrating AI, ML, and NLP into the SMS firewall can significantly impact the operator’s A2P messaging monetisation initiatives. By leveraging the power of AI technologies, SMS firewalls can reduce potential revenue losses by swiftly detecting suspicious messages, analysing network traffic patterns, and identifying sources of unauthorised traffic, which can then be processed according to the established firewall ruleset.
Furthermore, AI, ML, and NLP are constantly evolving, and integrating these technologies into SMS firewalls helps operators stay ahead of emerging threats and ensure that their messaging system remains secure and effective over time. As the volume of messaging traffic continues to grow, MNOs that leverage these advanced technologies will be better equipped to manage their messaging traffic and maximise their A2P messaging monetisation initiatives.
However, building an impregnable defence requires both technical ability and insight. Don’t hesitate to contact our experts today to find a tailored firewall solution that will help you get the most out of your monetisation efforts!