With our online transactions growing exponentially, detection and mitigation of frauds have become more complex than ever today. Similarly, with our digital lives becoming increasingly interconnected, significant and dependent, fraud has grown to great proportions. Here, having fraud detection systems is critical to delivering an unobstructed view of entities, hidden patterns and relations with an enterprise ecosystem. Therefore, let’s start with understanding what real-time fraud detection is.
Real-time fraud detection is the real-time execution of algorithms for detecting frauds to detect any kind of fraudulent activities on financial payment systems such as credit cards. It uses real-time data analysis like forensic analytics and preventive analytics for determining whether the ongoing transaction is legitimate or not. Even though the system is not perfect, it has greatly helped in reducing fraud losses.
Your relationship with your customers or business partners can be greatly impacted when dealing with fraudulent behaviour. Moreover, the complexity, as well as the speed of today’s information technology world, has significantly increased the risk of fraudulent behaviour more than ever. Here, the chances of fraud transactions became higher thus calling for an effective fraud detection system.
Another reason fraud detection is important is due to fraud has touched nearly every business. Whether you talk about data breaches affecting customers’ privacy rights and payment security or ransom attacks demanding huge sums of money from organizations, it has penetrated everywhere. If this is not dealt with proactively, fraud can affect the bottom line of the companies by pulling resources from core business and priorities, damaging brand reputation and affecting profits. It can cost human lives in extreme cases as well.
In the past, fraud detection and prevention efforts highly lagged behind the speed with which the fraudsters worked. However, fraudsters are still constantly moving targets. Previously, investigators were slow as cross-referencing data and findings manually was a heavy resource and time-consuming task. This also indicated a fragmented approach. That made it difficult for the investigators to generate deep analytics in real-time and monitor activity in all corners of the web while acting fast.
Here, new fraud detection and prevention technologies have leveraged a comprehensive approach by combining machine learning, AI, automation, predictive analytics and more, to identify new schemes and combat frauds as well. The technologies rely on real-time monitoring and usage ahead of the decision models. This way, they can process and categorize big data into meaningful categories or groups, learn from complex patterns and identify any abnormality. Further, these technologies can look into all kinds of surface activities, deep and dark web to gain a holistic view of fraudster activities. The rate of change in today’s online activities and transactions, and fraud means that fraud detection systems should be flexible and adaptable. This change is needed not only in logic but in system topology as well. You can always take the help of the best real-time fraud detection system to prevent fraud transactions.
