Leveraging Natural Language Processing for Trade Exception Classification and Resolution in Capital Markets: A Comprehensive Study

Abstract

In capital markets, trade exceptions pose serious problems that frequently lead to financial losses and operational inefficiencies. This study explores how to effectively classify and manage trade exceptions using Natural Language Processing (NLP). Our goal is to use machine learning models and sophisticated natural language processing techniques to automate exception management and enhance decision-making in trade operations. Data preparation, feature extraction, model training, and evaluation make up our methodology. According to experimental data, manual interventions have significantly decreased, improving operational correctness and efficiency. This study establishes the foundation for upcoming advancements in trade exception management and advances intelligent financial automation.