Linking Industry Sectors and Financial Statements: A Hybrid Approach for Company Classification

Guy Stephane Waffo Dzuyo (), Gaël Guibon (), Christophe Cerisara () and Luis Belmar-Letelier ()
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Guy Stephane Waffo Dzuyo: Forvis Mazars, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications - Inria - Institut National de Recherche en Informatique et en Automatique - CentraleSupélec - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique, SYNALP - Natural Language Processing : representations, inference and semantics - LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery - LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications - Inria - Institut National de Recherche en Informatique et en Automatique - CentraleSupélec - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique
Gaël Guibon: LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications - Inria - Institut National de Recherche en Informatique et en Automatique - CentraleSupélec - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique, LIPN - Laboratoire d'Informatique de Paris-Nord - CNRS - Centre National de la Recherche Scientifique - Université Sorbonne Paris Nord, SYNALP - Natural Language Processing : representations, inference and semantics - LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery - LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications - Inria - Institut National de Recherche en Informatique et en Automatique - CentraleSupélec - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique
Christophe Cerisara: SYNALP - Natural Language Processing : representations, inference and semantics - LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery - LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications - Inria - Institut National de Recherche en Informatique et en Automatique - CentraleSupélec - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique
Luis Belmar-Letelier: Forvis Mazars

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Abstract: The identification of the financial characteristics of industry sectors has a large importance in accounting audit, allowing auditors to prioritize the most important area during audit. Existing company classification standards such as the Standard Industry Classification (SIC) code allow to map a company to a category based on its activity and products. In this paper, we explore the potential of machine learning algorithms and language models to analyze the relationship between those categories and companies' financial statements. We propose a supervised company classification methodology and analyze several types of representations for financial statements. Existing works address this task using solely numerical information in financial records. Our findings show that beyond numbers, textual information occurring in financial records can be leveraged by language models to match the performance of dedicated decision tree-based classifiers, while providing better explainability and more generic accounting representations. We think this work can serve as a preliminary work towards semi-automatic auditing. Models, code, and a preprocessed dataset are publicly available for further research at https://github.com/WaguyMz/hybrid company classification

Keywords: Machine Learning; Industry Sectors; Large Language Models; LLM Applications; Audit; Financial Statement (search for similar items in EconPapers)
Date: 2025-02-25
New Economics Papers: this item is included in nep-acc, nep-ain and nep-big
Note: View the original document on HAL open archive server: https://hal.science/hal-05031499v1
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Published in The 39th Annual AAAI Conference on Artificial Intelligence (AAAI 2025), Feb 2025, Philadelphia (Pennsylvania), United States

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