Profitable businesses often rely on the analysis of extensive datasets to make informed decisions. In the insurance industry, underwriting is a particularly resource- intensive process that involves evaluating various risk factors. To streamline and enhance this process, a proposed system leverages the power of Natural Language Processing (NLP). This system's automation encompasses multiple key steps, including the conversion of PDF documents to text, pre-processing of documents, selective extraction of relevant information through custom corpora, and the classification of data into specific risk classes. Furthermore, a summarization component is employed to condense lengthy Attended Physician Statements (APS) into concise highlights of medical history, facilitating quicker decision-making. The classification module plays a pivotal role in simplifying the underwriting task, ensuring that the information extracted from PDFs is in a format that the system can readily process.