  Fraud causes substantial costs and losses for companies and clients in the
finance and insurance industries. Examples are fraudulent credit card
transactions or fraudulent claims. It has been estimated that roughly $10$
percent of the insurance industry's incurred losses and loss adjustment
expenses each year stem from fraudulent claims. The rise and proliferation of
digitization in finance and insurance have lead to big data sets, consisting in
particular of text data, which can be used for fraud detection. In this paper,
we propose architectures for text embeddings via deep learning, which help to
improve the detection of fraudulent claims compared to other machine learning
methods. We illustrate our methods using a data set from a large international
health insurance company. The empirical results show that our approach
outperforms other state-of-the-art methods and can help make the claims
management process more efficient. As (unstructured) text data become
increasingly available to economists and econometricians, our proposed methods
will be valuable for many similar applications, particularly when variables
have a large number of categories as is typical for example of the
International Classification of Disease (ICD) codes in health economics and
health services.
