Deep Learning in Insurance: An Incremental Deep Learning Approach for Pricing Prediction Strategy in the Insurance Industry

Alaa Tareq Mohamed, Riadh Ksantini and Jihene Kaabi

Chapter 11 in Artificial Intelligence and Beyond for Finance, 2024, pp 359-391 from World Scientific Publishing Co. Pte. Ltd.

Abstract: Deep learning is a type of machine learning known for its competitive advantage in discovering complex relationships in all data types. However, the insurance applications of deep learning were used for damage detection and churn prediction applications, while the premium prediction received low attention from previous researchers. This study aims to build an incremental deep learning model to predict insurance premiums. The model contributes to the previously studied Usage-Based Insurance (UBI) concept. We propose a deep learning model consistent with the UBI concept that considers the available factors affecting the premium to predict the insurance premium. The proposed model consists of two parts. Part one is the Convolutional Neural Network (CNN) for deep features extraction. Part two is the Support Vector Regression (SVR) built on the extracted deep features to predict the premium. The proposed model is called CNN-SVR after combining the two parts of CNN and SVR. The dataset was collected from an insurance company to train the proposed model and evaluate its performance compared to the other classical models adopted previously by other researchers, namely the Neural Network (NN), Random Forests (RF), Decision Trees (DT), Linear Regression and Support Vector Regression (SVR). The model performance evaluation was achieved using some metrics and the execution time needed to add a new data point to the model. The selected metrics are the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage of Error (MAPE), Explained Variance (EV), Correlation Coefficient (R), and t-test. The proposed CNN-SVR model reported the best averages among the other models of 1363.935 MSE, 36.838 RMSE, 18.774 MAE, 11.940 MAPE, 0.957 R, and 1 − p values close to 1 in the t-test. The proposed incremental model reported a faster execution time than the classical models, which need to be retrained fully to add a new data point. The study concluded that CNN-SVR model outperforms the other models in prediction performance and execution time, which supports the hypothesis. A possible future direction for this study is to use a larger dataset with more factors affecting the premium for a better contribution to the UBI and predictions.

Keywords: Artificial Intelligence; Machine Learning; Deep Learning; Reinforcement Learning; Sentiment Analysis; Portfolio Management; Financial Forecasting (search for similar items in EconPapers)
JEL-codes: C63 C8 G11 G17 (search for similar items in EconPapers)
Date: 2024
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