Big Data is a hot topic today that stems back to the early days of high-performance computing and parallel computing. These days, Big Data tools facilitate the ease in applying these concepts. Much of the discussion around Big Data focuses on the size of data, but not as much on the fact that it’s changing the analytics paradigm. That paradigm shift centers around analytics “living in the stream”.
Streaming analytics is no stranger to FICO, and one of the best examples is with its fraud detection solution, FICO® Falcon® Fraud Manager. Falcon models rely on transaction profiles that summarize data in the stream as it passes by, in order to compute the pertinent fraud feature variables without relying on the persistence of data in production.
Another major impact of Big Data is that analytics must reduce reliance on persistent data, and allow analytic models to adjust on the fly in the stream. To meet the need of an increasingly dynamic stream, FICO has focused research efforts on self-learning techniques such as adaptive analytics and self-calibrating analytics. FICO strongly believes these are critical technologies to supplement traditionally trained neural network fraud models, which rely on persistent data. Self-learning technologies may even eventually replace neural networks in some regions.