by Jerome Kehrli
Posted on Friday Sep 03, 2021 at 11:17AM in Big Data
(Article initially published on NetGuardians' blog)
NetGuardians overcomes the problems of analyzing billions of pieces of data in real time with a unique combination of technologies to offer unbeatable fraud detection and efficient transaction monitoring without undermining the customer experience or the operational efficiency and security in an enterprise-ready solution.
When it comes to data analytics, the more data the better, right? Not so fast. That’s only true if you can crunch that data in a timely and cost-effective way.
This is the problem facing banks looking to Big Data technology to help them spot and stop fraudulent and/or non-compliant transactions. With a window of no more than a hundredth of a millisecond to assess a transaction and assign a risk score, banks need accurate and robust real-time analytics delivered at an affordable price. Furthermore, they need a scalable system that can score not one but many thousands of transactions within a few seconds and grow with the bank as the industry moves to real-time processing.
AML transaction monitoring might be simple on paper but making it effective and ensuring it doesn’t become a drag on operations has been a big ask. Using artificial intelligence to post-process and analyze alerts as they are thrown up is a game-changing paradigm, delivering a significant reduction in the operational cost of analyzing those alerts. But accurate fraud risk scoring is a much harder game. Some fraud mitigation solutions based on rules engines focus on what the fraudsters do, which entails an endless game of cat and mouse, staying up to date with their latest scams. By definition, this leaves the bank at least one step behind.
At NetGuardians, rather than try to keep up with the fraudsters, we focus on what we know and what changes very little – customers’ behavior and that of bank staff. By learning “normal” behavior, such as typical time of transaction, size, beneficiary, location, device, trades, etc., for each customer and internal user, and comparing each new transaction or activity against those of the past, we can give every transaction a risk score.Read More