Behavioral analytics use machine learning to understand and anticipate behaviors at a granular level across each aspect of the life cycle of a policy. The information is tracked in profiles that represent the behaviors of each individual, merchant, account and device. These profiles are updated with each transaction, in real time, to compute analytic characteristics that provide informed predictions of future behavior.

Profiles contain details of monetary and non-monetary transactions. Non-monetary may include a change of address, a request to change beneficiary status or banking details. Monetary transaction details support the development of patterns that may represent an individual’s typical behavior across multiple industry products, the days when someone tends to logged a claim, and the time period between geographically disperse premium payment locations, to name a few examples. Profiles are very powerful as they supply an up- to-date view of activity used to avoid transaction abandonment caused by frustrating false positives.

Given the sophistication and speed of organized fraud rings, behavioral profiles must be updated with each transaction. This is a key component of helping financial institutions anticipate individual behaviors and execute fraud detection strategies, at scale, which distinguish both legitimate and illicit behavior changes.