  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 includes also 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.
