# Explainable Machine Learning for Fraud Detection

arXiv: https://arxiv.org/abs/2105.06314 · HTML: https://mlsanity.com/paper/2105.06314/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 20 references that are in the map.
- First posted on arXiv 13 May 2021.

## Builds on (most cited first, 7 of 7)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection](https://mlsanity.com/paper/1903.06661/) (2019)
- [Explaining Anomalies Detected by Autoencoders Using SHAP](https://mlsanity.com/paper/1903.02407/) (2019)
- [Contextual Outlier Interpretation](https://mlsanity.com/paper/1711.10589/) (2017)
- [Shapley Values of Reconstruction Errors of PCA for Explaining Anomaly Detection](https://mlsanity.com/paper/1909.03495/) (2019)
- [Instance-Level Explanations for Fraud Detection: A Case Study](https://mlsanity.com/paper/1806.07129/) (2018)

## Built on by (most cited first, 9 of 9)

- [TempME: Towards the Explainability of Temporal Graph Neural Networks via Motif Discovery](https://mlsanity.com/paper/2310.19324/) (2023)
- [Foiling Explanations in Deep Neural Networks](https://mlsanity.com/paper/2211.14860/) (2022)
- [Explainability in Practice: A Survey of Explainable NLP Across Various Domains](https://mlsanity.com/paper/2502.00837/) (2025)
- [RESHAPE: Explaining Accounting Anomalies in Financial Statement Audits by enhancing SHapley Additive exPlanations](https://mlsanity.com/paper/2209.09157/) (2022)
- [Error Analysis of Shapley Value-Based Model Explanations: An Informative Perspective](https://mlsanity.com/paper/2404.13522/) (2024)
- [Evaluating Local Model-Agnostic Explanations of Learning to Rank Models with Decision Paths](https://mlsanity.com/paper/2203.02295/) (2022)
- [ExMo: Explainable AI Model using Inverse Frequency Decision Rules](https://mlsanity.com/paper/2205.10045/) (2022)
- [Explanation Method for Anomaly Detection on Mixed Numerical and Categorical Spaces](https://mlsanity.com/paper/2209.04173/) (2022)
- [IsoEx: an explainable unsupervised approach to process event logs cyber investigation](https://mlsanity.com/paper/2306.09260/) (2023)

## Abstract (arXiv)

The application of machine learning to support the processing of large datasets holds promise in many industries, including financial services. However, practical issues for the full adoption of machine learning remain with the focus being on understanding and being able to explain the decisions and predictions made by complex models. In this paper, we explore explainability methods in the domain of real-time fraud detection by investigating the selection of appropriate background datasets and runtime trade-offs on both supervised and unsupervised models.