To protect users in this age of digital uncertainty, the creation of a global protection ecosystem is imminent. Businesses around the world are utilising cutting-edge technologies such as Artificial Intelligence (AI) and Machine Learning (ML), to mention few of, to make sure cybersecurity.

How artificial intelligence (AI) and machine learning (ML) can help businesses detect fraud?

Artificial Intelligence as well as Machine Learning are being used in almost every industry. Through instant detection, these technologies have emerged as important tools for avoiding fraudulent activities. While e-commerce websites use algorithms to recommend products to customers based on their interests, cloud contact centre solutions use them to provide real-time assistance to customers. A cloud contact centre solution combines multiple communication channels, such as WhatsApp, SMS, and email, into a single suite, allowing for seamless customer support across all these channels while maintaining a comprehensive record collection.

Rule-based fraud detection systems and methods can detect obvious fraudulent scenarios such as unusual account numbers and transaction types, whereas artificial intelligence (AI) and machine learning (ML) can detect hidden data correlations. Furthermore, ML enables the development of an algorithm that processes large datasets faster and with less manual labour. Here are a few examples of how technological advancements are assisting businesses.

• Spam filtering and fraud prevention are two applications of ML-powered algorithms.
• To conduct new fraudulent activities and avoid any existing countermeasures, fraudsters constantly update their hacking parameters. To provide spam filtering while protecting the network from unwarranted exposure, AI-powered algorithms adjust in tandem with spammers' new hacking parameters.
• AI assists in the prevention of fraud by categorising data and flagging anomalies. Fake SMS and SMS spoofs, for example, use standard formats to exploit vulnerabilities, which can be identified by comparing them to legitimate broadcasts (Mobile Network Operators).

How is artificial intelligence transforming the customer experience?

According to surveys, fraud is a criterion for improving the customer experience. It is preferable if fraudulent activities are kept to a minimum. This inverse relationship is at the heart of risk management and cybersecurity innovation, as threats such as hacking have grown exponentially with the availability of sensitive information.

The user experience improves exponentially when AI-based bots replace human interaction for good contact such as procedural querying – finding account balances, assisting with onboarding processes, and general questions such as store opening hours. It saves both business professionals and customers time. This enhances the overall experience by providing real-time response 24 hours a day, 365 days a year.

In managing A2P SMS fraud, the SMS firewall and machine learning are used.
With the proliferation of AI-powered chatbots, there has been a significant increase in application-to-person (A2P) messaging, and SMS remains the most popular A2P channel since it is simple, cost efficient, and supported by every mobile phone generation, from feature phones to the most recent smartphones. Information disbursement has become automated for faster communication, leading to an increase in fraudulent threats that harm end-users as well as businesses and mobile network operators. Phishing and Malware are two existing and new cases of fraud in the SMS ecosystem that steal sensitive data by tricking users into providing details on a fake page or infecting mobile devices with malware. For example, many users across the country were victims of the Flubot Scam last year, in which they did receive SMS notifications about missed, voice mails, deliveries, and photo uploads. When a user clicks on a link to download or access something, their device becomes infected with a specific type of malware. Another example of SMS fraud is spam and artificial traffic inflation, which bothers users while also inflating SMS traffic between operators domestically and internationally, increasing the cost. As a result, businesses employ SMS firewall solutions to tackle spam, unwanted, grey, and fraudulent traffic. The technology was created to secure mobile networks and identify SMS vulnerabilities.

Simply put, it is a method of providing full protection and control over network messaging. Messages are routed through the firewall, where they are analysed and filtered as needed. The most recent advancement in this existing process is the inclusion of ML-powered detection methods, which could facilitate protection against network breaches through accurate monitoring and proactive responses. Furthermore, ML enables businesses to stay ahead of fraud detection by providing the quickest methods of detecting fraudulent behaviour, even heavily manipulated text, or message masking, and filtering out anomalous activity in real-time without the possibility of human error. As a result, the opportunities for fraudsters to exploit a vulnerability in message signalling protocols could be greatly reduced.

The future of user engagement is mobile identity.

Mobile Identity is another important aspect of fraud detection. Businesses will be able to easily verify customers via their mobile phone number at every stage of the journey, from account activation and onboarding to payment and app download, by utilising Mobile Identity. This all happens in the background, securely and silently, without requiring the customer to enter a verification code. Silent Mobile Verification, which allows customers to verify users in a smooth and unobtrusive manner, and SIM-Swap check, which captures real-time insights to see if a mobile phone number has been swapped, protect customers from this rising form of fraud.

Mobile Identity and similar future-facing technology solutions combined with AI and ML processes can help enterprises improve, facilitate seamless transactions, and detect fraud on  a massive scale by managing millions of customers or network data points.

Although AI-powered algorithms are being used to detect and prevent fraud, their learning curve is nascent. The limitations lie in the data set provided to them. With inefficient data come insufficient solutions, making the system incapable of performing the designated functions. Much research goes into fool-proofing the security mechanisms to build a robust and dynamic infrastructure, promising enough to safeguard users from cyberattacks and identify loopholes in the system to plug the vulnerabilities.