Predicting data tampering using augmented machine learning models

Abstract

Certain aspects involve using a set of machine learning modeling models for predicting attempts to tamper with records using a fraudulent dispute. A tampering prediction system receives a request from a target entity to modify event data for a historical event, including information about the target entity and the event. The system generates a first score by applying a first set of machine learning models to the information from the request and information about the target entity obtained from a database. They system computes a second score by applying a second machine learning model to event data retrieved from the database. The second machine learning model has been trained using labeled training data and is augmented with a model that has been trained using unlabeled training data. The system generates an overall score for the request based on the first score and the second score.