Deep Learning-based Prediction of Risk in Life Insurance

The insurance sector depends on precise risk predictions to execute satisfactory evaluations and decisions for policy assessment. Standardized actuarial methods face difficulties in managing the intricate irregular relations that exist between healthcare records and personal activities and population data. This deep learning approach implements diverse data resources for upgrading life insurance risk evaluation processes. The proposed model employs Insurance Company Benchmark (COIL 2000) dataset by executing a sequence of data preprocessing techniques that include normalization alongside encoding along with feature selection for making the data fit for training purposes. A Multilayer Perceptron (MLP) neural network applies its architecture to discover hidden data patterns which lead to superior risk classification outcomes. The model shows capability to boost underwriting operations and promote evidence-based choice-making. Artificial intelligence leads to modernize traditional risk assessment methods through this work which provides practical settings for applying predictive analytics in life insurance operations.