Auto insurance fraud prediction model based on improved particle swarm optimization BP neural network

Linlin Yuan; Wenan Tan

doi:10.1117/12.2628462

22 April 2022 Auto insurance fraud prediction model based on improved particle swarm optimization BP neural network

Linlin Yuan, Wenan Tan

Proceedings Volume 12174, International Conference on Internet of Things and Machine Learning (IoTML 2021); 121740Y (2022) https://doi.org/10.1117/12.2628462
Event: International Conference on Internet of Things and Machine Learning (IoTML 2021), 2021, Shanghai, China

Abstract

With the booming of the insurance industry, the number of insurance frauds has increased gradually, among which the fraud in the auto insurance industry is the most serious. In order to improve the BP neural network prediction is easy to form local minimal value and not the global optimum, convergence speed is slow and the prediction accuracy is low, etc., we propose an improved particle swarm optimization (PSO) algorithm based on the dynamic linear decreasing adjustment of the weights in the particle swarm algorithm, and then the BP neural network weights and thresholds are optimized to establish the auto insurance fraud prediction based on the improved particle swarm optimization BP neural network model. The 400 sets of sample data of 13 main influencing variables of auto insurance historical claims of an insurance company were selected for PSO-BP and BP neural network model training, and 50 sample data were used for the prediction accuracy evaluation of the optimized model. The results show that based on the improved PSO-BP neural network algorithm its mean square error between the predicted and true values is significantly reduced and the prediction accuracy is greatly improved, which can effectively predict auto insurance fraud.

Citation Download Citation

Linlin Yuan and Wenan Tan "Auto insurance fraud prediction model based on improved particle swarm optimization BP neural network", Proc. SPIE 12174, International Conference on Internet of Things and Machine Learning (IoTML 2021), 121740Y (22 April 2022); https://doi.org/10.1117/12.2628462

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available