Transaction Data
Paygilant employs propriety transaction behavioral maps. The Behavioral Maps represent the purchasing patterns/behavior of a specific customer and her nearest neighbors and are created using Paygilant’s proprietary machine learning algorithms. The behavioral maps typically comprise a large amount of information but must be compact 9 enough since they are securely transmitted to the mobile device. To achieve this Paygilant utilizes its depth of field (DOF) approach from digital photography to compress the information so that complex calculations that do not require work intensive CPU and memory. A Behavioral Map shows a clear, high resolution picture of the different risk zones and is a key factor in determining the risk of a specific transaction and has the following key characteristics: – User specific: each map is unique, calculated and maintained on a per user basis, therefore representing a transaction risk level for each customer’s transaction. – Lightweight: Resolution variations enable maintaining only the necessary data, reducing the map’s weight to a bare minimum. – Dynamic: As the purchase behavior changes, the map will be modified.
