The Classification of Insurance Claim Risk Using the Multilayer Perceptron Method

Abstract

Policyholders purchase insurance policies to protect themselves or their assets from potential financial risks in the future. Insurance guarantees that if an event covered by the policy occurs, the insurance company will provide compensation according to the agreed terms. Insurance companies conduct risk assessments for each policyholder to determine the premium that must be paid, making it essential to classify risk categories accurately. The Multilayer Perceptron (MLP) is one method used for classification problems. It is a machine learning algorithm belonging to the family of artificial neural networks. MLP is a flexible algorithm that can solve various classification problems, including those with complex features and non-linear relationships between input and output variables. The result of this research is the development and implementation of a Multilayer Perceptron method to classify risk categories. The evaluation of the Multilayer Perceptron model for risk classification shows satisfactory performance. Based on the classification report from training and test data, the model does not exhibit overfitting or underfitting.