Detection of Automobile Insurance Fraud Using Feature Selection and Data Mining Techniques

Author:

Subudhi Sharmila1,Panigrahi Suvasini1

Affiliation:

1. Veer Surendra Sai University of Technology, Burla, India

Abstract

This article presents a novel approach for fraud detection in automobile insurance claims by applying various data mining techniques. Initially, the most relevant attributes are chosen from the original dataset by using an evolutionary algorithm based feature selection method. A test set is then extracted from the selected attribute set and the remaining dataset is subjected to the Possibilistic Fuzzy C-Means (PFCM) clustering technique for the undersampling approach. The 10-fold cross validation method is then used on the balanced dataset for training and validating a group of Weighted Extreme Learning Machine (WELM) classifiers generated from various combinations of WELM parameters. Finally, the test set is applied on the best performing model for classification purpose. The efficacy of the proposed system is illustrated by conducting several experiments on a real-world automobile insurance defraud dataset. Besides, a comparative analysis with another approach justifies the superiority of the proposed system.

Publisher

IGI Global

Subject

General Medicine

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Comparative Study of Machine Learning Techniques for Insurance Fraud Detection;Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC);2024-08-06

2. Fraud Detection Using Decision Tree Algorithm to Curb Identity Theft;Lecture Notes in Networks and Systems;2023

3. Insurance Fraud Detection Using Machine Learning;International Journal of Advanced Information and Communication Technology;2021-01-05

4. A Study on Customer’s Preference for Personal Vehicle Insurance Provided by Different Insurance Companies in Bengaluru;International Journal of Management, Technology, and Social Sciences;2020-08-22

5. Fast Unsupervised Automobile Insurance Fraud Detection Based on Spectral Ranking of Anomalies;International Journal of Engineering;2020-07

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