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.
@article{arxiv.1808.10543,
title = {A Self-Attention Network for Hierarchical Data Structures with an Application to Claims Management},
author = {Leander Löw and Martin Spindler and Eike Brechmann},
journal= {arXiv preprint arXiv:1808.10543},
year = {2018}
}