Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodings
This work addresses the problem of missing categorical-rich datasets for researchers in anomaly detection, though it is incremental as it focuses on dataset creation and encoding comparisons.
The authors tackled the lack of benchmark datasets for anomaly detection in auditing data by introducing the Vehicle Claims dataset, which includes fraudulent insurance claims with many categorical attributes, and they evaluated various encoding methods and anomaly detection approaches, finding that GEL encoding and embedding layers helped mitigate dimensionality issues.
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.