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Using Artificial Intelligence against fraudulent activities


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After the spread of the COVID-19 catastrophe, many societal and consumer behavioral changes ensued. With lockdowns put in place overnight, businesses and educational institutions were forced to continue their operations remotely. This phenomenon led to an inevitable surge in the adoption of technologies for routine tasks. As a result, the country witnessed an increased attempts and incidences of digital fraud. Since the beginning of the outbreak in March 2020, the attempts of fraudulent digital transactions rose by over 28% between March 2020 & 2021 compared to the previous year.

In this age of digital uncertainty, creating a global protection ecosystem is imminent to protect users. Businesses globally are using the latest technologies, including Artificial Intelligence (AI) and Machine Learning (ML), to name a few, to ensure cybersecurity.

How technologies like AI & ML aid businesses in fraud detection 

Artificial Intelligence and Machine Learning are being employed in almost all fields. These technologies have emerged as significant tools to avoid fraudulent activities through instant detection. While e-commerce websites deploy the algorithms to recommend products to consumers based on their interests, cloud contact center solutions use them to assist the customer in real-time. A cloud contact center solution combines various communication channels like WhatsApp, SMS, Email, etc. into one umbrella suite to allow seamless customer support across those channels while having a comprehensive record compilation.

Rule-based fraud detection systems and method can identify obvious fraudulent scenarios like unusual account number, transaction types, while technologies like AI and ML can find hidden data correlations. Additionally, ML allows creating an algorithm that processes large datasets and does it faster, including lesser manual work. Here are some ways in which technological advancements are helping businesses.

  • ML-powered algorithms are being used for spam filtering and fraud prevention.
  • Fraudsters constantly update their hacking parameters to conduct new fraudulent activities and to avoid any existent countermeasures. AI-powered algorithms adjust hand-in-hand with spammers’ new hacking parameters to provide spam filtering while protecting the network from unwarranted exposure.
  • AI helps prevent fraud by categorizing the data and flagging the anomalies. For instance, fake SMS and SMS spoofs have standard formats to exploit the vulnerabilities, which can be identified by corroborating them with legitimate broadcasts (Mobile Network Operators).

How is AI transforming customer experience?          

Surveys suggest that fraud is a criterion for improving customer experience. It is better with minimized fraudulent activities. This inverse relationship forms the basis of innovation in risk management and cybersecurity as threats such as hacking have increased manifold with the accessibility of sensitive information.

The user experience improves exponentially with AI-based bots that bypass human interaction for standard communication such as procedural querying – finding account balances, helping with onboarding processes, and general questions like store opening times. It saves the time of both business professionals and the customers. This improves the overall experience through real-time response 24/7, round the year.

The SMS firewall and ML in managing A2P SMS frauds

With the proliferation of AI-based chatbots, there is a significant increase in application-to-person (A2P) messaging, and SMS remains the most popular A2P channel because it is simple, cost effective, and supported by every mobile phone generation, from feature phones to the latest smartphones. For faster communication, information disbursement has become automated, resulting in an increase in fraudulent attacks that harm end-users as well as enterprises and mobile network operators. Some of the existing and new fraud cases in the SMS ecosystem include Phishing & Malware that steal sensitive data by tricking user to supply details on fake page or by infecting mobile device with malware. For example, last year, many users across regions were victims of the Flubot Scam, in which they received SMS notifications about missed calls, voice mails, deliveries, and photo uploads. The user’s device is infected with a specific type of malware the moment they click on a link to download or access something. Another case of SMS fraud is Spam and Artificial Traffic Inflation, which annoys users but importantly inflates SMS traffic between operators domestically, and internationally and thus increasing the cost. Therefore, the companies use SMS firewall solutions to combat spam, unwanted, grey, and fraudulent traffic. The technology was developed to secure mobile networks and find vulnerabilities in SMS. To put it simply, it is a way of providing complete protection and control over messaging on the network. The messages are routed through the firewall and are analyzed and filtered accordingly. The latest advancement in this existing process is the inclusion of ML-powered detection methods through which protection against breaches across networks via accurate monitoring and proactive responses could be facilitated. Furthermore, ML enables companies to stay ahead of fraud detection as it provides the fastest ways of identifying fraudulent behaviour, even heavily manipulated text, or text masking, and filtering out suspicious traffic in real-time without the possibility of human error. Hence, the opportunities for scammers to exploit a vulnerability in message signaling protocols could be controlled to a great extent.

Mobile identity – Future of user engagement

Another important aspect of fraud detection is Mobile Identity. By employing Mobile Identity, businesses will be able to easily verify customers via their mobile phone number across every stage of the journey, from account activation and onboarding to payment and app download. This all happens securely and silently in the background, without requiring the customer to enter a verification code. The solutions available within Mobile Identity include Silent Mobile Verification, meaning customers can verify users in a smooth and unobtrusive way and SIM-Swap check, which captures real time insights to see if a mobile phone number has been swapped, protecting 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.



Views expressed above are the author’s own.