Macroeconomic Modeling for Insurance Applications using the ANN-SVM Method

In the insurance and financial industries, comonotonicity has proven to be an effective instrument for addressing both theoretical and practical issues during the past ten years. This research summarises the literature on comonotonicity theory and its practical applications, focussing on the latter's merits. It delves into the systematic process of selecting algorithms, preparing data, extracting features, and training models in order to create an efficient model for submitting insurance applications. Data merging with the target and source sets is the first step in the process. Preprocessing involves normalising continuous data and using a one-hot encoder to transform categorical variables. The most important part of the process, feature extraction, uses four kinds of higherorder statistics to make the model better. The suggested model, which combines ANN-SVM, achieves much better results than individual ANN-SVM models. Findings show a significant increase in accuracy, reaching 97.51%, making it an excellent choice for jobs involving the submission of insurance applications. These results highlight the significance of feature extraction for insurance industry decision-making and machine learning model optimisation. This research adds to the efficiency of insurance application processing and improves risk assessment procedures by utilising modern approaches. It offers a potential strategy for industry developments.