Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodings.

Ajay Chawda, Stefanie Grimm, Marius Kloft

Keywords: Unsupervised, Anomaly Detection, Auditing Data

TL;DR: This paper introduces a synthetic dataset created on the domain knowledge of insurance claims and analyzes unsupervised methods and various categorical encodings on the data.

Abstract:

In this paper, we introduce the Vehicle Claims dataset, consisting of fraudulent insurance claims for automotive repairs. The data belongs to the more broad category of Auditing data, which also includes Journals and Network Intrusion data. Insurance claim data are distinctively different from other auditing data (such as network intrusion data) in their high number of categorical attributes. We tackle the
common problem of missing benchmark datasets for anomaly detection: datasets are mostly confidential, and the public tabular datasets do not contain relevant and sufficient categorical attributes. Therefore, a large-sized dataset is created for this purpose and referred to as Vehicle Claims (VC) dataset. The dataset is evaluated on shallow and deep learning methods. Due to the introduction of categorical
attributes, we encounter the challenge of encoding them for the large dataset. As One Hot encoding of high cardinal dataset invokes the "curse of dimensionality", we experiment with GEL encoding and embedding layer for representing categorical attributes. Our work compares competitive learning, reconstruction-error, density estimation and contrastive learning approaches for Label, One Hot, GEL encoding
, and embedding layer to handle categorical values.

Community Implementations: [ 6 code implementations](https://www.catalyzex.com/paper/unsupervised-anomaly-detection-for-auditing/code)