# Adaptive Stress Testing for Adversarial Learning in a Financial Environment

arXiv: https://arxiv.org/abs/2107.03577 · HTML: https://mlsanity.com/paper/2107.03577/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 8 Jul 2021.

## Builds on (most cited first, 3 of 3)

- [Adaptive Stress Testing for Autonomous Vehicles](https://mlsanity.com/paper/1902.01909/) (2019)
- [A Survey of Credit Card Fraud Detection Techniques: Data and Technique Oriented Perspective](https://mlsanity.com/paper/1611.06439/) (2016)
- [Adaptive Stress Testing of Trajectory Predictions in Flight Management Systems](https://mlsanity.com/paper/2011.02559/) (2020)

## Built on by (most cited first, 2 of 2)

- [Adversarial Learning in Real-World Fraud Detection: Challenges and Perspectives](https://mlsanity.com/paper/2307.01390/) (2023)
- [FraudBench: Protocol-Sensitive Benchmarking of Adversarial Robustness for Financial Risk Assessment](https://mlsanity.com/paper/2608.24551/) (2026)

## Abstract (arXiv)

We demonstrate the use of Adaptive Stress Testing to detect and address potential vulnerabilities in a financial environment. We develop a simplified model for credit card fraud detection that utilizes a linear regression classifier based on historical payment transaction data coupled with business rules. We then apply the reinforcement learning model known as Adaptive Stress Testing to train an agent, that can be thought of as a potential fraudster, to find the most likely path to system failure -- successfully defrauding the system. We show the connection between this most likely failure path and the limits of the classifier and discuss how the fraud detection system's business rules can be further augmented to mitigate these failure modes.