Narrow Results

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.

In the past decade, the insurance business has experienced remarkable growth, as evidenced by the introduction of numerous novel products such as chatbots, digital claims, telemetric insurance and robo-advisors. The insurance industry is also experiencing a significant transformation resulting from the digital transformation. The use of digital technology opens the door for inventive architectures and the development of innovative products in the future that will benefit both insurance companies and their clients. By integrating Internet of Things devices, digital innovation revolutionizes how insurance companies collaborate with industries, ultimately benefiting all parties. This chapter reviews the history of digital innovation in insurance, also known as insurance technology (InsurTech), as well as how technology has underpinned the expansion of InsurTech in recent years. The chapter also examines previous and current studies on the use of technology in insurance transactions and how the implementation of digitalization affects the industry’s long-term viability. The research identified three key InsurTech concepts that have revolutionized the insurance sector. New entrants could disrupt the entire insurance distribution process and introduce new consumer value propositions to attract and retain clients, challenging current business models. InsurTech improves incumbent insurers’ value chain by providing innovative technologies and solutions that boost efficiency and reduce costs.