  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.
