Cognitive Data Underwriting

The automation of underwriting processes in the insurance industry could significantly enhance operational efficiency by reducing the manual evaluation of risk factors. This paper presented a Natural Language Processing (NLP)based system designed to streamline the underwriting workflow. The system automated key steps such as converting PDF documents into text, pre-processing documents, and selectively extracting relevant data using custom corpora. A classification module was implemented to categorize extracted information into predefined risk classes, optimizing the evaluation process. Additionally, a summarization component condensed lengthy Attending Physician Statements (APS) into concise summaries, enabling quicker and more informed decision-making. The proposed system demonstrated its potential to simplify underwriting tasks, transforming complex documents into actionable insights for risk assessment.