Use of artificial intelligence against fraudulent activities
After the spread of the COVID-19 disaster, many changes in societal and consumer behavior ensued. With the shutdowns put in place overnight, businesses and educational institutions have been forced to continue their operations remotely. This phenomenon has led to an inevitable increase in technology adoption for routine tasks. As a result, the country has seen an increase in digital fraud attempts and incidences. Since the start of the outbreak in March 2020, attempted fraudulent digital transactions increased by more than 28% between March 2020 and 2021 compared to the previous year.
In this era of digital uncertainty, the creation of a global protection ecosystem is imminent to protect users. Companies around the world are using the latest technologies, including artificial intelligence (AI) and machine learning (ML), to name a few, to ensure cybersecurity.
How Technologies Like AI and ML Help Businesses Detect Fraud
Artificial intelligence and machine learning are used in almost every field. These technologies have become important tools to prevent fraudulent activities through instantaneous 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 such as WhatsApp, SMS, email, etc. in an umbrella suite to enable seamless customer support across these channels while having a full compilation of records.
Rules-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. Moreover, ML makes it possible to create an algorithm that processes large data sets and does it faster, including less manual work. Here are some ways technological advancements are helping businesses.
- ML-powered algorithms are used for spam filtering and fraud prevention.
- Fraudsters are constantly updating their hacking settings to conduct new fraudulent activities and avoid any existing countermeasures. AI-powered algorithms adjust hand-in-hand with new spammer hacking settings to provide spam filtering while protecting the network from unwarranted exposure.
- AI helps prevent fraud by categorizing data and flagging anomalies. For example, fake SMS and SMS spoofing have standard formats for exploiting vulnerabilities, which can be identified by corroborating them with legitimate broadcasts (mobile network operators).
How is AI transforming the customer experience?
Surveys suggest that fraud is a criterion for improving the 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 with the accessibility of sensitive information.
User experience improves exponentially with AI-powered bots that bypass human interaction for standard communication such as procedural queries – finding account balances, helping with onboarding processes and questions general information such as store opening hours. This saves time for professionals and customers. This enhances the overall experience with real-time response 24/7, all year round.
The SMS and ML Firewall in A2P SMS Fraud Management
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 all generations of mobile phones, from multifunction phones to the latest smartphones. For faster communication, the dissemination of information has become automated, leading to an increase in fraudulent attacks that harm end users as well as businesses and mobile network operators. Some of the existing and new instances of fraud in the SMS ecosystem include phishing and malware that steals sensitive data by tricking the user into providing details on a fake page or infecting the mobile device with malware. For example, last year, many users from all regions fell victim to the Flubot scam, in which they received SMS notifications about missed calls, voicemails, 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 mostly inflates SMS traffic between national and international operators and thus increases the cost. Therefore, companies use SMS firewall solutions to fight against spam, unwanted, gray and fraudulent traffic. The technology was developed to secure mobile networks and find vulnerabilities in text messages. To put it simply, it is a way to provide complete messaging protection and control over the network. Messages are routed through the firewall and are scanned and filtered accordingly. The latest advancement in this existing process is the inclusion of ML-powered detection methods through which protection against breaches on networks through accurate monitoring and proactive responses could be facilitated. Additionally, ML allows businesses to stay ahead of fraud detection as it provides the fastest ways to identify fraudulent behavior, even heavily manipulated text or text obfuscation, and filter the suspicious traffic in real time without the possibility of human error. Therefore, the opportunities for crooks to exploit a vulnerability in message signaling protocols could be controlled to a large extent.
Mobile Identity – The Future of User Engagement
Mobile identity is another important aspect of fraud detection. Using Mobile Identity, businesses will be able to easily verify customers via their mobile phone number at every step of the journey, from account activation and onboarding to payment and app download. This all happens securely and silently in the background, without the customer having to enter any verification code. Solutions available in Mobile Identity include Silent Mobile Verification, which means customers can verify users smoothly and discreetly, and SIM-Swap Verification, which captures real-time information to see if a mobile phone number has been traded, thereby protecting customers from this growing form. of fraud.
Mobile identity and similar forward-looking technology solutions, combined with AI and ML processes, can help businesses improve, facilitate seamless transactions, and detect fraud at scale by managing millions of clients or network data points.
Although AI-powered algorithms are used to detect and prevent fraud, their learning curve is incipient. The limitations lie in the data set provided to them. Inefficient data comes with insufficient solutions, rendering the system unable to perform the designated functions. Much research is devoted to testing security mechanisms to build a robust and dynamic infrastructure that is promising enough to protect users against cyberattacks and identify system flaws to close vulnerabilities.
The opinions expressed above are those of the author.
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