Medical insurance fraud identification based on isolation loss and deep autoencoder
Aiming at the problem of high similarity and low degree of discrimination between fraudulent samples and normal samples and the confusion of marginal normal samples in med-ical insurance fraud identification,this paper proposes a medical insurance fraud identification algorithm based on isolation loss and deep autoencoder(ISDAE).Aiming at the easy isolation of marginal fraud samples and sparse fraud samples,the algorithm proposes a sample isolation measure to quantitatively analyze the differences between the two types of samples from the perspective of feature distribution.On the basis,using DAE's ability to mine linear and non-linear features of medical insurance and considering the interference of margin normal samples on model training,an isolation loss is defined in the latent space to achieve the aggregation of center normal samples and the separation of edge normal samples,thereby increasing the differ-ence between fraudulent samples and normal samples.To further improve the fraud detection performance of the model,the fraud degree of samples is evaluated by integrating the isolation value and the reconstruction error.Finally,the performance of the proposed algorithm is verified on the Tianchi medical insurance dataset.The results show that the overall fraud identification performance of the proposed ISDAE algorithm is better than the comparative methods,and its performance is more stable.
medical insurance fraud identificationisolation lossdeep autoencoderunsuper-vised learning
NETL
NSTL
万方数据
