To achieve the goal, we developed a system that uses machine learning techniques to classify the claims received by market and entities, products, and matters claimed. This process began with the collection and cleaning of available data, i.e., claims with their corresponding classifications. Then, we used classification algorithms and neural networks to analyze the information, test alternative models and hyperparameter combinations until achieving the expected results.

Our solution also included the development of a web service to enable interoperability of the proposed system, i.e., to be consumed and integrated by the client entity’s systems. Additionally, we developed scalability elements of the solution through automation and scalability practices known as MLOps, providing capabilities for continuous model retraining and identifying issues that could affect the solution in a production environment.