How Accessing and Assessing Data in Real Time Can Prevent Fraud and Breaches
A security breach is one of those issues that can be hard to fix once the horse has left the stable. Many high-profile companies have had their reputations damaged because of data breaches. A ding to a reputation is especially painful when it’s paired with high legal fees and hefty payouts. Data breaches are occurring at a record pace. The threats that enterprises face today are stealthy, sophisticated and quick. Luckily, so are data analytics platforms. Big data can play a major role in fraud detection and prevention if you know how to wield its capabilities. It’s important to weed out the misconceptions that could be holding you back from embracing big data as a powerful tool to keep threats at bay.
Common Misconceptions About Big Data and Fraud Prevention
The first big misconception that many enterprise leaders and IT decision makers have about using big data for fraud protection is that it’s necessary to have huge amounts of data. The assumption is often that only enterprises that take in large pools of data on an hourly, daily or monthly basis have any business exploring or investing in big data to prevent and stop fraud. Large pools of data can definitely be useful for establishing baselines and detecting patterns that could have menacing causes. However, any amount of data that you have can be screened using data analytics. Doing this properly all comes down to choosing the right streaming platform.
The second big misconception regarding the role big data plays in fraud protection is that data can only be used for detection after the fact. This is completely untrue. In fact, applying analytics to data in motion can actually allow you to take action in real time and as a breach is happening. Most breaches don’t happen all at once. Digital breadcrumbs are created the moment there is a breach or information is accessed from a new or unapproved source. Many standard security programs only offer alerts once a breach has already been successful. An enterprise is then placed in a defensive position to try to manually isolate the issue and prevent it from spreading. Big data can be used to automatically trigger responses if certain actions occur or if red flags are raised. This can then give decision makers time and resources to evaluate the situation and take further action with the knowledge that the breach has been cut off at the source. What’s more, small warning signs can prompt action before a breach is fully executed and precious information has been siphoned from an enterprise’s servers.
The third big misconception about using big data for fraud detection is that it will disrupt service or add complexities to an enterprise’s internal and external online environments. Many IT decision makers envision roadblocks, constant prompts for credentials and lag times when they think about adding a platform that uses data to search for threats. However, the streaming nature of the right big data platforms ensures smoothness. Real-time data architecture seamlessly collects, integrates, dissects and acts upon data as it is processed.
Big Data Offers Big Protection
A big data platform essentially acts like a strainer that stops harmful objects from passing through along with all of the data that is streaming into a network. Those harmful objects are then left at the bottom of the strainer for an enterprise to examine. How should companies get started with finding the right method for using big data for fraud prevention and detection? It’s important to look for a system that can produce reliable, accurate data and then act in real time based on what that high-quality data reveals. Features like sub-second latency, self-healing capabilities, enterprise-grade reliability and scale-out architecture are essential if you want a system that’s effective and usable. Clear alerts, reports and visuals that are delivered to the user can end up being the difference between acting in time to stop fraud and letting a breach spread throughout an entire digital ecosystem.