Research on Integrated Learning Fraud Detection Method Based on Combination Classifier Fusion (THBagging): A Case Study on the Foundational Medical Insurance Dataset

Jibing Gong,Weixia Du,Hekai Zhang

doi:10.3390/electronics9060894

Abstract

In recent years, the number of fraud cases in basic medical insurance has increased dramatically. We need to use a more efficient method to identify the fraudulent users. Therefore, we deploy the cloud edge algorithm with lower latency to improve the security and enforceability in the operation process. In this paper, a new feature extraction method and model fusion technology are proposed to solve the problem of basic medical insurance fraud identification. The feature second-level extraction algorithm proposed in this paper can effectively extract important features and improve the prediction accuracy of subsequent algorithms. In order to solve the problem of unbalanced simulation allocation in the medical insurance fraud identification scenario, a sample division method based on the idea of sample proportion equilibrium is proposed. Based on the above methods of feature extraction and sample division, a new training and fitting model fusion algorithm (tree hybrid bagging, THBagging) is proposed. This method makes full use of the balanced idea of the tree model algorithm based on Boosting to fuse, and finally achieves the effect of improving the accuracy of basic medical insurance fraud identification.

Highlights

With the growing consolidation and improvement of medical insurance of China, more than 1.3 billion people [1,2] are sharing the social dividends in the developed process
We propose a novel idea of sample equalization to deal with the problem of category imbalance in medical insurance identification
Aiming at the difficulty of feature selection in the basic medical insurance fraud recognition scenario, we propose a second-level feature extraction algorithm based on the classification tree model, which uses the path information represented by the leaf nodes in the tree model to compress and represent various user behavior

Summary

Introduction

Basic medical insurance (the term “basic medical insurance” in this paper can be shortly denoted “medical insurance”) fraud refers to deceiving insurance personnel to obtain insurance compensation through insurance or fictitious or exaggerated insurance injuries [3]. This behavior infringes the rights of others and seriously harms social health. The behavior of those who commit medicare fraud is variable, and criminal methods are constantly emerging, which makes it difficult to identify fraud through intuitive judgment [4,5]. Through continuous accumulation of medical insurance data, data mining [6] and machine learning [7] technologies can be used to analyze massive data to find the potential rules of fraudsters, and effectively identify the real fraudsters

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