Top Online Fraud Trends to Watch Out For This Year
The digital world has significantly transformed over time along with security measures. Fraud is inevitable. Even after tight security measures, cyberlaw, fraud-detection algorithms, and digital scrutiny, there are multiple instances of frauds occurring all over the world. Even with advances in digital technologies, the average time to detect fraud is still measured in days or […]READ MORE >>
The digital world has significantly transformed over time along with security measures. Fraud is inevitable. Even after tight security measures, cyberlaw, fraud-detection algorithms, and digital scrutiny, there are multiple instances of frauds occurring all over the world. Even with advances in digital technologies, the average time to detect fraud is still measured in days or months. The problem is that fraudsters have become so skillful by bringing about variations in cyber attacks that existing perimeter or endpoint fraud solutions are no more sufficient. So what can we expect from institutions and fraudsters in their battle for security?
As the digital security measures get sophisticated so will the fraudsters’ method of attack. Online fraud can be compared to trail and error process since by using millions of different combinations, the fraudster can gain unauthorized access. They somehow find loopholes in the new system and outwit them. After the introduction of EMV, online merchants have come under increasing pressure to strengthen themselves against online fraud. Although encryption systems like RSA, SHA256, and AES are uncrackable even by the best machines on earth, fraudsters use other methods to bypass such systems.
Use of social media
It’s surprising how much of personal information is contained within social media itself. Fraudsters can use social media to examine their target’s online activities and learn more about them to engineer an automated scheme and collect the required information. Consumers should be more aware of sharing their information on the social network. Additionally, social media platforms are also enhancing their own security mechanism to avoid a breach of user data, a case similar to Cambridge Analytica. Since consumers have multiple social media platforms to choose from, their sensitive information may be compromised.
Replacement for rule-based systems
Currently, most of the proactive fraud prevention takes place using rules-based systems. Fraudsters of the past had limited means so had to work individually, and as a result, rules-based fraud prevention system worked as a limited and simple dataset was involved. However, that is not the case anymore, as present-day fraudsters use sophisticated techniques, which can easily beat rules-based systems. Another flaw of a rule-based system is that it can also decline legitimate orders, which are carried out by the actual customers. It causes the company to lose their customer as their transaction doesn’t get processed when they needed it the most.
The word IoT has been emphasized enough. IoT and connected devices aim to simplify the lives of the consumers. However, they do pose security risks of their own. For instance, an IoT connected fridge can monitor the contents of it and buying patterns, whereas IoT connected cars can track the location, distance, and places visited. Companies investing in the IoT technologies should address such potential risks and tighten their security measures to ensure that the customer data remains safe.
Advanced machine learning
Advanced machine learning has been in the headlines recently for numerous reasons including beating Go champion and being able to accurately classify images. With the abundance of datasets available today, advanced machine learning algorithms can automate processing tasks. Online fraud prevention is also one such area, which can do good with machine learning. Learning consumer behavior from across millions of transaction and fraudulent activities, fraud analytics, and machine learning algorithms can help accurately detect online fraud instantly.
To know more about trends in online fraud, fraud analytics, and machine learning algorithm: