# FraudFox: Adaptable Fraud Detection in the Real World

arXiv: https://arxiv.org/abs/2603.13014 · HTML: https://mlsanity.com/paper/2603.13014/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 41 references that are in the map.
- First posted on arXiv 13 Mar 2026.

## Builds on (most cited first, 4 of 4)

- [Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking](https://mlsanity.com/paper/1706.06691/) (2017)
- [Mondrian Forests: Efficient Online Random Forests](https://mlsanity.com/paper/1406.2673/) (2014)
- [An Online Learning Approach to Generative Adversarial Networks](https://mlsanity.com/paper/1706.03269/) (2017)
- [Ensemble Sales Forecasting Study in Semiconductor Industry](https://mlsanity.com/paper/1705.00003/) (2017)

## Abstract (arXiv)

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 x investigations are feasible', or `at most y 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 automatically and continuously update our weights for each 'oracle'. For the second problem, we show how to derive an optimal decision surface, and how to compute the Pareto optimal set, to allow what-if questions. An important consideration is adaptation: Fraudsters will change their behavior, according to our past decisions; thus, we need to adapt accordingly. The resulting system, method, is scalable, adaptable to changing fraudster behavior, effective, and already in production at Amazon. FraudFox augments a fraud prevention sub-system and has led to significant performance gains.