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Title: Auto insurance fraud identification based on a CNN-LSTM fusion deep learning model

Authors: Huosong Xia; Yanjun Zhou; Zuopeng Zhang

Addresses: School of Management, Wuhan Textile University, Wuhan, China; Research Center of Enterprise Decision Support, Key Research Institute of Humanities and Social Sciences, Universities of Hubei Province, Wuhan, China ' School of Management, Wuhan Textile University, Wuhan, China ' Coggin College of Business, University of North Florida, Jacksonville, FL 32224, USA

Abstract: The traditional auto insurance fraud identification method relies heavily on feature engineering and domain knowledge, making it difficult to accurately and efficiently identify fraud when the amount of claim data is large and the data dimension is high. Deep learning models have strong generalisation abilities and can automatically complete feature extraction. This paper proposes a deep learning model for auto insurance fraud identification by combining convolutional neural network (CNN), long- and short-term memory (LSTM), and deep neural network (DNN). Our proposed method can extract more abstract features and help avoid the complex feature extraction process that is highly dependent on domain experts in traditional machine learning algorithms. Experiments demonstrate that our method can effectively improve the accuracy of auto risk fraud identification.

Keywords: auto insurance fraud; deep learning; CNN-LSTM.

DOI: 10.1504/IJAHUC.2022.120943

International Journal of Ad Hoc and Ubiquitous Computing, 2022 Vol.39 No.1/2, pp.37 - 45

Received: 12 Jun 2020
Accepted: 15 Aug 2020

Published online: 18 Feb 2022 *

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