# PermuteAttack: Counterfactual Explanation of Machine Learning Credit Scorecards

arXiv: https://arxiv.org/abs/2008.10138 · HTML: https://mlsanity.com/paper/2008.10138/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2020.

## Built on by (most cited first, 16 of 16)

- [If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniques](https://mlsanity.com/paper/2103.01035/) (2021)
- [Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data](https://mlsanity.com/paper/2101.08030/) (2021)
- [DualCF: Efficient Model Extraction Attack from Counterfactual Explanations](https://mlsanity.com/paper/2205.06504/) (2022)
- [Counterfactual Shapley Additive Explanations](https://mlsanity.com/paper/2110.14270/) (2021)
- [Model-agnostic and Scalable Counterfactual Explanations via Reinforcement Learning](https://mlsanity.com/paper/2106.02597/) (2021)
- [Feature Importance Guided Attack: A Model Agnostic Adversarial Attack](https://mlsanity.com/paper/2106.14815/) (2021)
- [Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions](https://mlsanity.com/paper/2111.01235/) (2021)
- [A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures](https://mlsanity.com/paper/2404.00673/) (2024)
- [Investigating Imperceptibility of Adversarial Attacks on Tabular Data: An Empirical Analysis](https://mlsanity.com/paper/2407.11463/) (2024)
- [Unpacking the Black Box: Regulating Algorithmic Decisions](https://mlsanity.com/paper/2110.03443/) (2021)
- [A comprehensive interpretable machine learning framework for Mild Cognitive Impairment and Alzheimer's disease diagnosis](https://mlsanity.com/paper/2412.09376/) (2024)
- [Fair Recourse for All: Ensuring Individual and Group Fairness in Counterfactual Explanations](https://mlsanity.com/paper/2601.20449/) (2026)
- [Knowledge Distillation-Based Model Extraction Attack using GAN-based Private Counterfactual Explanations](https://mlsanity.com/paper/2404.03348/) (2024)
- [MISLEAD: Manipulating Importance of Selected features for Learning Epsilon in Evasion Attack Deception](https://mlsanity.com/paper/2404.15656/) (2024)
- [Adversarial training for tabular data with attack propagation](https://mlsanity.com/paper/2307.15677/) (2023)
- [Semantic Prioritization in Visual Counterfactual Explanations with Weighted Segmentation and Auto-Adaptive Region Selection](https://mlsanity.com/paper/2511.12992/) (2025)

## Abstract (arXiv)

This paper is a note on new directions and methodologies for validation and explanation of Machine Learning (ML) models employed for retail credit scoring in finance. Our proposed framework draws motivation from the field of Artificial Intelligence (AI) security and adversarial ML where the need for certifying the performance of the ML algorithms in the face of their overwhelming complexity poses a need for rethinking the traditional notions of model architecture selection, sensitivity analysis and stress testing. Our point of view is that the phenomenon of adversarial perturbations when detached from the AI security domain, has purely algorithmic roots and fall within the scope of model risk assessment. We propose a model criticism and explanation framework based on adversarially generated counterfactual examples for tabular data. A counterfactual example to a given instance in this context is defined as a synthetically generated data point sampled from the estimated data distribution which is treated differently by a model. The counterfactual examples can be used to provide a black-box instance-level explanation of the model behaviour as well as studying the regions in the input space where the model performance deteriorates. Adversarial example generating algorithms are extensively studied in the image and natural language processing (NLP) domains. However, most financial data come in tabular format and naive application of the existing techniques on this class of datasets generates unrealistic samples. In this paper, we propose a counterfactual example generation method capable of handling tabular data including discrete and categorical variables. Our proposed algorithm uses a gradient-free optimization based on genetic algorithms and therefore is applicable to any classification model.