WEAKLY-SUPERVISED FRAUD DETECTION FOR TRANSPORTATION SYSTEMS VIA MACHINE LEARNING

摘要

Example methods and systems disclosed herein train an accurate machine-learned model that detects fraud within an electronic transportation system. A first model is trained on a first (comparatively small) set of trip data items representing trips taken, or requested, in the electronic transportation system. The first set of trip data items have been manually labeled by human analysts to determine whether the trips were or were not fraudulent. The first model is used to generate weak labels for a second (comparatively larger) set of trip data items that lack manual labels. The weak labels are used along with the second set of trip data items to train a second model that is more accurate than the first model for detecting fraud.