What is fraud analytics? Most fraud models consist of hundreds of manual rules and logic statements that describe behaviors associated with fraud. Traditionally, analysts had to write complex statements or SQL queries to encode these rules. With Simility’s manual rule builder, multiple data transforms are precomputed, meaning anyone can quickly and easily write a simple rule that captures extremely complex logic and test it on their company’s own data. The fraud-fighting power of your rules engine will be in the hands of the people with the most experience and intuition in detecting fraud—your own analysts.
Simility allows analysts to create signals and manual rules that codify their insights, which are then fed into Simility’s machine-learning models. Through big-data predictive analytics, your fraud model will evolve based on every decision and rule, continuously enhancing the detection of anomalous patterns. This means your model improves its defenses while you sleep. Simility’s machine-learning engine begins with experience from an array of business verticals, then quickly adapts to your unique needs. It detects patterns of fraud before they are perceptible to human analysis or become a problem, keeping you ahead of the fraudsters.
Any piece of data can be useful in detecting fraud and abuse, but not all fraud detection solutions let you import all types of data into your fraud model. Simility accepts unstructured data, so you can programmatically detect patterns in ambiguous pieces of information, such as the tone of an email or the appearance of an image. Your predictive model is built on top of Simility’s foundation of known fraudsters, so you get the advantage of our extensive experience from day one.