  Insurance companies must manage millions of claims per year. While most of
these claims are non-fraudulent, fraud detection is core for insurance
companies. The ultimate goal is a predictive model to single out the fraudulent
claims and pay out the non-fraudulent ones immediately. Modern machine learning
methods are well suited for this kind of problem. Health care claims often have
a data structure that is hierarchical and of variable length. We propose one
model based on piecewise feed forward neural networks (deep learning) and
another model based on self-attention neural networks for the task of claim
management. We show that the proposed methods outperform bag-of-words based
models, hand designed features, and models based on convolutional neural
networks, on a data set of two million health care claims. The proposed
self-attention method performs the best.
