Extraction and Classification of Financial Documents: State of the Art, Methodology and Perspectives

Authors

Fousseni Seri, Université Nazi BONI (UNB), Burkina Faso

Sadouanouan Malo, Université Nazi BONI (UNB), Burkina Faso

Julie Thiombiano, Ecole Polytechnique de Ouagadougou (EPO), Burkina Faso

Published in

IST-Africa 2026 Conference Proceedings

ISSN: 2576-8581

ISBN: 978-1-905824-76-2

DOI: https://doi.org/10.67725/IST-Africa.2026.EDXZ9507

Publisher

IST-Africa Institute and IIMC International Information Management Corporation Ltd

Published in Ireland

Abstract

Supreme Audit Institutions (SAIs) are crucial for transparency in public financial management but face major challenges when manually processing large volumes of multilingual, semi-structured, and heterogeneous financial documents. This paper reviews recent advances in multimodal Transformers and OCR-free models for document artificial intelligence and proposes a modular and reproducible pipeline for financial document analysis in African SAI contexts. The pipeline integrates layout-aware models (LayoutLMv3, DocFormer) and OCR-free architectures (Donut) and is validated on public benchmarks and a proprietary Francophone dataset (NOVAFRIQ). Experimental results show up to 93.7% F1-score for information extraction and 95.2% accuracy for document classification. Pilot simulations indicate potential processing time reductions of 60-75% and error decreases of approximately 40%. The study demonstrates a promising approach for building robust, explainable, and multilingual AI-assisted auditing tools.

Keywords

Financial Document Analysis, Supreme Audit Institutions, Multimodal Models, Document Classification, Information Extraction, OCR-free Models

Cite this paper

F. Seri, S. Malo and J. Thiombiano (2026) "Extraction and Classification of Financial Documents: State of the Art, Methodology and Perspectives", IST-Africa 2026 Conference Proceedings, Miriam Cunningham and Paul Cunningham (Eds), IST-Africa Institute and IIMC, 2026, ISSN: 2576-8581, ISBN: 978-1-905824-76-2, https://doi.org/10.67725/IST-Africa.2026.EDXZ9507

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