Developing a life insurance recommendation system using machine learning methods

In the last 10 years, the use of advanced technologies and big data handling methods in the field of artificial intelligence has led to an increase in the number of machine learning-based projects in many sectors and domain such as personalized product offerings that enhance customer loyalty and business value. Algorithm based development has been ongoing for 70 years and continues to grow. The use of machine learning techniques in the insurance industry has the potential to greatly improve customer satisfaction and increase company profitability. In this study, by collecting and analyzing data on the portfolio movements, payment behavior, and demographic characteristics of existing product owners, predictive models were conducted to identify potential customers for cross-selling. This study followed data preprocessing steps, including handling missing data, detecting, and repairing outliers, and preprocessing categorical data for use in the model. The prediction problem was treated as a classification problem, and explanatory data analysis and correlation analysis were performed to gain a deeper understanding of the data. The results of this study could be used to inform future efforts to personalize product offerings and increase sales in the insurance industry. The prediction problem was addressed using supervised learning methods, including Decision trees, Logistic regression, Random forest algorithms, Naive Bayes and Gradient boosting algorithms. The performance of the models was optimized through scenario-based experiments, and the effects of various data preprocessing steps, such as normalization and dimensionality reduction, on model performance were observed. The performance of the models was evaluated using a range of metrics, including accuracy, AUC, and F-1 scores. The results of this study suggest that hyperparameter tuning can play a significant role in improving the performance of machine learning models in this context. Overall, the use of machine learning techniques has the potential to greatly enhance the accuracy of predictions and improve decision-making in the insurance industry.