Leveraging large language models for document classification in the banking sector

Abstract Document classification serves as foundational step in critical tasks such as information extraction, analysis and decision-making. However, existing approaches often struggle with the variability, volume, and complexity of real-world documents. These methods are further limited by a lack of configurability and explainability, requiring specialized technical expertise to accommodate diverse user needs and often producing results that are difficult to interpret. To address the complexities of modern document processing, this paper introduces a novel zero-shot document classification framework that leverages Large Language Models (LLMs), designed for accessibility and configurability by both technical and non-technical users. Unlike traditional methods, which require extensive labeled data, the zero-shot configuration enables our framework to perform the classification task without any prior exposure to labeled examples of the target categories, relying instead on semantic understanding derived from user-provided label descriptions and document content. Developed and validated using a real-world banking dataset, our framework leverages different strategies for providing context to LLMs during classification. Experimental results demonstrate substantial improvements in both accuracy and efficiency, outperforming current zero-shot methods while also reducing operating costs.