Benchmarking Offline Reinforcement Learning Algorithms for E-Commerce Order Fraud Evaluation

TL;DR

This paper benchmarks offline reinforcement learning algorithms for e-commerce order fraud evaluation, demonstrating their superiority over traditional classification methods in a simulated environment and proposing a novel training approach.

Contribution

It introduces the application of offline RL to order fraud evaluation, compares various algorithms, and proposes a new training loss for better policy alignment.

Findings

Offline RL outperforms traditional classification in fraud detection

Proposed training loss improves policy action accuracy

Offline RL is suitable for high-stakes e-commerce applications

Abstract

Amazon and other e-commerce sites must employ mechanisms to protect their millions of customers from fraud, such as unauthorized use of credit cards. One such mechanism is order fraud evaluation, where systems evaluate orders for fraud risk, and either "pass" the order, or take an action to mitigate high risk. Order fraud evaluation systems typically use binary classification models that distinguish fraudulent and legitimate orders, to assess risk and take action. We seek to devise a system that considers both financial losses of fraud and long-term customer satisfaction, which may be impaired when incorrect actions are applied to legitimate customers. We propose that taking actions to optimize long-term impact can be formulated as a Reinforcement Learning (RL) problem. Standard RL methods require online interaction with an environment to learn, but this is not desirable in high-stakes applications like order fraud evaluation. Offline RL algorithms learn from logged data collected from the environment, without the need for online interaction, making them suitable for our use case. We show that offline RL methods outperform traditional binary classification solutions in SimStore, a simplified e-commerce simulation that incorporates order fraud risk. We also propose a novel approach to training offline RL policies that adds a new loss term during training, to better align policy exploration with taking correct actions.