Automating Financial Risk Assessment Using NLP and Machine Learning

Abstract

Financial risk assessment is a critical function within the financial industry, encompassing the identification, measure- ment, and mitigation of various risks such as credit risk, market risk, operational risk, and liquidity risk. Traditional methods often rely on quantitative models built upon struc- tured numerical data, which, while effective, frequently over- look the vast amount of unstructured information available in financial documents. This paper explores the integration of Natural Language Processing (NLP) and Machine Learn- ing (ML) techniques to automate and enhance financial risk assessment. We propose a comprehensive framework that leverages NLP to extract meaningful insights from diverse unstructured textual data sources, including financial news, company reports, social media, and regulatory filings. These extracted features, combined with traditional quantitative data, are then fed into advanced machine learning models to provide more accurate, timely, and holistic risk evalua- tions. Our approach aims to overcome the limitations of existing models by providing a more accurate, timely, and in- terpretable solution for financial market analysis, ultimately leading to more robust decision-making and improved fi- nancial stability. We demonstrate how NLP can identify early warning signals, detect emerging risks, and provide a nuanced understanding of market sentiment and corporate health, which are often missed by purely numerical analyses. The integration of ML models further allows for the identi- fication of complex patterns and predictive capabilities that enhance the overall risk assessment process.