An explainable attention network for fraud detection in claims management
针对健康保险理赔数据中欺诈检测的挑战，提出一种基于深度学习的可解释注意力网络，利用文本分类方法处理变长输入和多类别变量，在德国私人健康保险数据上优于传统机器学习模型。
Insurance companies must manage millions of claims per year. While most of these are not fraudulent, those that are nevertheless cost insurance companies and those they insure vast amounts of money. The ultimate goal is to develop a predictive model that can single out fraudulent claims and pay out non-fraudulent ones automatically. Health care claims have a peculiar data structure, comprising inputs of varying length and variables with a large number of categories. Both issues are challenging for traditional econometric methods. We develop a deep learning model that can handle these challenges by adapting methods from text classification. Using a large dataset from a private health insurer in Germany, we show that the model we propose outperforms a conventional machine learning model. With the rise of digitalization, unstructured data with characteristics similar to ours will become increasingly common in applied research, and methods to deal with such data will be needed.