# Explaining Anomalies using Denoising Autoencoders for Financial Tabular Data

arXiv: https://arxiv.org/abs/2209.10658 · HTML: https://mlsanity.com/paper/2209.10658/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 50 references that are in the map.
- First posted on arXiv 21 Sep 2022.

## Builds on (most cited first, 25 of 25)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [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)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Deep Neural Networks and Tabular Data: A Survey](https://mlsanity.com/paper/2110.01889/) (2021)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [Interpretable machine learning: definitions, methods, and applications](https://mlsanity.com/paper/1901.04592/) (2019)
- [Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders](https://mlsanity.com/paper/1807.02011/) (2018)
- [Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey](https://mlsanity.com/paper/2006.11371/) (2020)
- [Techniques for Interpretable Machine Learning](https://mlsanity.com/paper/1808.00033/) (2018)
- [Why do tree-based models still outperform deep learning on tabular data?](https://mlsanity.com/paper/2207.08815/) (2022)
- [Handling Incomplete Heterogeneous Data using VAEs](https://mlsanity.com/paper/1807.03653/) (2018)
- [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)
- [Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Models](https://mlsanity.com/paper/1805.06230/) (2018)
- [Model Agnostic Multilevel Explanations](https://mlsanity.com/paper/2003.06005/) (2020)
- [Shapley Values of Reconstruction Errors of PCA for Explaining Anomaly Detection](https://mlsanity.com/paper/1909.03495/) (2019)
- [Detection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Networks](https://mlsanity.com/paper/1709.05254/) (2017)
- [Robust Variational Autoencoders for Outlier Detection and Repair of Mixed-Type Data](https://mlsanity.com/paper/1907.06671/) (2019)
- [Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features](https://mlsanity.com/paper/1804.04888/) (2018)
- [Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks](https://mlsanity.com/paper/1908.00734/) (2019)
- [Multi-view Contrastive Self-Supervised Learning of Accounting Data Representations for Downstream Audit Tasks](https://mlsanity.com/paper/2109.11201/) (2021)
- [Solving the "false positives" problem in fraud prediction](https://mlsanity.com/paper/1710.07709/) (2017)
- [An Explainable Autoencoder For Collaborative Filtering Recommendation](https://mlsanity.com/paper/2001.04344/) (2019)

## Built on by (most cited first, 4 of 4)

- [Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Data](https://mlsanity.com/paper/2404.14933/) (2024)
- [Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking](https://mlsanity.com/paper/2504.20900/) (2025)
- [Tabular Data Adapters: Improving Outlier Detection for Unlabeled Private Data](https://mlsanity.com/paper/2504.20862/) (2025)
- [ODBAE: a high-performance model identifying complex phenotypes in high-dimensional biological datasets](https://mlsanity.com/paper/2211.03054/) (2022)

## Abstract (arXiv)

Recent advances in Explainable AI (XAI) increased the demand for deployment of safe and interpretable AI models in various industry sectors. Despite the latest success of deep neural networks in a variety of domains, understanding the decision-making process of such complex models still remains a challenging task for domain experts. Especially in the financial domain, merely pointing to an anomaly composed of often hundreds of mixed type columns, has limited value for experts. Hence, in this paper, we propose a framework for explaining anomalies using denoising autoencoders designed for mixed type tabular data. We specifically focus our technique on anomalies that are erroneous observations. This is achieved by localizing individual sample columns (cells) with potential errors and assigning corresponding confidence scores. In addition, the model provides the expected cell value estimates to fix the errors. We evaluate our approach based on three standard public tabular datasets (Credit Default, Adult, IEEE Fraud) and one proprietary dataset (Holdings). We find that denoising autoencoders applied to this task already outperform other approaches in the cell error detection rates as well as in the expected value rates. Additionally, we analyze how a specialized loss designed for cell error detection can further improve these metrics. Our framework is designed for a domain expert to understand abnormal characteristics of an anomaly, as well as to improve in-house data quality management processes.