Decoupling Decision-Making in Fraud Prevention through Classifier Calibration for Business Logic Action

arXiv:2401.05240v2 Announce Type: replace
Abstract: Machine learning models typically focus on specific targets like creating classifiers, often based on known population feature distributions in a business context. However, models calculating individual features adapt over time to improve precision, introducing the concept of decoupling: shifting from point evaluation to data distribution. We use calibration strategies as strategy for decoupling machine learning (ML) classifiers from score-based actions within business logic frameworks. To evaluate these strategies, we perform a comparative analysis using a …

abstract adapt arxiv business business logic classifier classifiers concept context cs.lg decision evaluation feature features focus for business fraud fraud prevention logic machine machine learning machine learning models making population precision prevention targets through type