Author: Caitlin Streamer
Manager, Field Data Science, Seattle
Caitlin has a diverse background in technology consulting, most recently as a data scientist in the natural language processing space. She approaches everything with a sharp and inquisitive mind and always loves learning about new technologies.
How Customer Outcomes Data Powers Customer Success
What is customer outcomes data? At its most simple level, a customer outcome is the result of knowing if a query to Ekata was associated with a “good” or “bad” event related to fraud that the customer might face. For example, if an ecommerce customer queries Ekata on transactional orders within their fraud decisioning workflow, […]
Using Machine Learning for Rule Building
Many of our customers use rules in their fraud workflows to identify good and bad actors. These rules, which are combinations of conditional statements that operate as data filters, can be very effective, but formulating “the right rule” can be difficult. That’s where the Ekata Field Data Science (FDS) team comes in to help customers […]