Developing a hybrid model based on Convolutional Neural Network (CNN) and Linear Discriminant Analysis (LDA) for investigating anti-selection risk in insurance

This paper proposes a hybrid Convolutional Neural Network (CNN) and Linear Discriminant Analysis (LDA) model that combines a deep convolutional neural network and linear discriminant analysis to investigate anti-selection risk in insurance markets. The model enhances risk assessments using extensive data from insurance companies' big data sources and advanced machine learning algorithms. This improves the detection of anti-selection tendencies and enhances overall risk management techniques. After the final convolution layer, we add a Linear Discriminant Analysis layer to the backbone model Convolutional Neural Network. The Linear Discriminant Analysis layer allows the model to gather features, minimizing variation within each class and maximizing separation between different classes. After the Linear Discriminant Analysis layer, we append a fresh, fully connected (FC) layer with softmax activation and made comprehensive adjustments. We employ both Convolutional Neural Network and Linear Discriminant Analysis models to extract features and perform classification. The hybrid Convolutional Neural Network (CNN) and Linear Discriminant Analysis (LDA) model demonstrate superior reliability, with a test accuracy score of 97.4%, surpassing the classification accuracy of the Convolutional Neural Network and Linear Discriminant Analysis models with 90.2% and 91.3%, respectively.