The analysis of unstructured financial documents is key to the capital markets supervision performed by Comissão de Valores Mobiliários (Brazilian SEC or CVM). Systems capable of reducing human effort involved in the task of screening documents and outlining relevant information, for further manual review, are important tools for CVM to deal with the shortage of human resources and expansion of the Brazilian securities market. In this regard, this dissertation presents and discusses the application of several machine learning algorithms and text processing techniques to perform two natural language processing tasks— document classification and information extraction—in a real market supervision environment. In the classification exercise, classic algorithms achieved a better performance than deep neural networks, which was enhanced by applying undersampling techniques and ensembles. Using the tested algorithms can improve the current precision rate from 20 percent–40 percent to more than 90 percent. The BERT network architecture was able to extract information from financial documents on capital increase and mergers. The successful results obtained in both tasks encourage future implementation of the studied models in the form of a decision support system. Another contribution of this work is the CVMCorpus, a corpus built to produce datasets for the tasks, with financial documents released between 2009 and 2019 by Brazilian companies, which opens possibilities of future linguistic and finance research.