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Activity Number: 204 - Experimental Design
Type: Contributed
Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #311020
Title: Generalization of Thompson Sampling for Multiple Categorical and Numerical Variables with Application for Fraud Detection
Author(s): Alex Zolotovitski*
Companies: T-Mobile
Keywords: reinforcement learning; Thompson Sampling; Fraud Detection

Thompson Sampling is a well-known effective algorithm of reinforcement learning in cases when the probability of reward depends on one categorical variable. Using a combination of unsupervised and supervised learning methods, we generalized the algorithm for the case when the reward depends on multiple categorical and numerical variables, tuned it with a simulation, and applied it to a fraud detection audit.

The method demonstrated good cumulative gain: checking 50% of candidate cases selected by the algorithm we could detect 96% of fraud cases (96% true positive rate) having 99% of related monetary loss (maximum possible reward).

Authors who are presenting talks have a * after their name.

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