Studies from University of North Florida Add New Findings in the Area of Ubiquitous Computing (Auto Insurance Fraud Identification Based On a Cnn-lstm Fusion Deep Learning Model): Ubiquitous Computing

2022 MAR 29 (NewsRx) -- By a News Reporter-Staff News Editor at NewsRx Policy and Law Daily -- Fresh data on Ubiquitous Computing are presented in a new report. According to news reporting out of Jacksonville, Florida, by NewsRx editors, research stated, “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.”

Financial supporters for this research include National Natural Science Foundation of China (NSFC), Research Center of Enterprise Decision Support, Key Research Institute of Humanities and Social Sciences in Universities of Hubei Province.

Our news journalists obtained a quote from the research from the University of North Florida, “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.”

According to the news editors, the research concluded: “Experiments demonstrate that our method can effectively improve the accuracy of auto risk fraud identification.”

This research has been peer-reviewed.

For more information on this research see: Auto Insurance Fraud Identification Based On a Cnn-lstm Fusion Deep Learning Model. International Journal of Ad Hoc and Ubiquitous Computing, 2022;39:37-45. International Journal of Ad Hoc and Ubiquitous Computing can be contacted at: Inderscience Enterprises Ltd, World Trade Center Bldg, 29 Route De Pre-Bois, Case Postale 856, Ch-1215 Geneva, Switzerland.

Our news journalists report that additional information may be obtained by contacting Zuopeng Zhang, University of North Florida, Coggin College of Business, Jacksonville, FL 32224, United States. Additional authors for this research include Huosong Xia and Yanjun Zhou.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1504/IJAHUC.2022.120943. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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