FraudFox: Adaptable Fraud Detection in the Real World
Matthew Butler, Yi Fan, and Christos Faloutsos

TL;DR
FraudFox is an adaptable, scalable fraud detection system that dynamically combines risk assessments, optimizes decision-making under constraints, and adapts to evolving fraudster behavior, proven effective in Amazon's production environment.
Contribution
This work introduces FraudFox, a novel system that integrates dynamic oracle-weighting, optimal decision surfaces, and Pareto analysis for real-world fraud detection and resource management.
Findings
Implemented in Amazon, improves fraud detection performance
Effectively adapts to changing fraudster strategies
Enhances resource allocation for investigations
Abstract
The proposed method (FraudFox) provides solutions to adversarial attacks in a resource constrained environment. We focus on questions like the following: How suspicious is `Smith', trying to buy $500 shoes, on Monday 3am? How to merge the risk scores, from a handful of risk-assessment modules (`oracles') in an adversarial environment? More importantly, given historical data (orders, prices, and what-happened afterwards), and business goals/restrictions, which transactions, like the `Smith' transaction above, which ones should we `pass', versus send to human investigators? The business restrictions could be: `at most investigations are feasible', or `at most $ lost due to fraud'. These are the two research problems we focus on, in this work. One approach to address the first problem (`oracle-weighting'), is by using Extended Kalman Filters with dynamic importance weights, to…
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Taxonomy
TopicsImbalanced Data Classification Techniques · Explainable Artificial Intelligence (XAI) · Spam and Phishing Detection
