CODM: an outlier detection method for medical insurance claims fraud. (28th November 2019)

• Record Type: Journal Article
• Authors: Gao, Yongchang; Guan, Haowen; Gong, Bin
• Is Part Of: International journal of computational science and engineering. Volume 20:Number 3(2019)
• Page Start: 404
• Page End: 411
• Publication Date: 2019-11-28
• Subjects: high dimensional -- feature bagging -- k-nearest neighbours -- data mining -- outlier detection -- medical insurance claims fraud; Computer science -- Mathematics -- Periodicals; Computer simulation -- Mathematical aspects -- Periodicals; Computational intelligence -- Periodicals; 004.015105
• Languages: English
• ISSNs: 1742-7185

Abstract

Data is the high dimensional in medical insurance claims management, and there are both dense and sparse regions in these datasets, so traditional outlier detection methods are not suitable for these data. In this paper, we propose a novel method to detect the outliers for abnormal medical insurance claims. Our method consists of three core steps - feature bagging to reduce the dimensions of data, calculating the core of the object's k-nearest neighbours, and computing the outlier score for each object by measuring the amount of movement of core by sequentially increasing k. Experimental results demonstrate our method is promising to tackle this problem.