Volume 1 Issue 3 | 2024 | View PDF
Paper Id:IJMSM-V1I3P105
doi: 10.71141/30485037/V1I3P105

Transformer-Based Classification of Financial Documents in Hybrid Workflows

Kiran Kumar Pappula

Citation:
Kiran Kumar Pappula, "Transformer-Based Classification of Financial Documents in Hybrid Workflows" International Journal of Multidisciplinary on Science and Management, Vol. 1, No. 3, pp. 48-61, 2024.

Abstract:
The increased number and the sophistication of the financial documents in the enterprise, especially those performing in the mixed physical-digital workflows, offer huge challenges to the automated classification of documents. MLmodels. Traditional machine learning models have limited success when faced with unstructured layouts, formats, and noisy data like scanned or handwritten records. The paper examines transformer-based models (BERT and LayoutLM) which has been used to generate a robust and intelligent financial document which would classify financial documents in such a heterogeneous environment. We introduce a framework which combines layout-sensitive embeddings, contextual language understanding and the structural analysis of documents to improve the accuracy and the applicability of the classification. We aim to have architecture that can operate smoothly as part of enterprise automation pipelines that accept document variability, partial corruption of data, as well as format changes frequently experienced in reality. We perform a case study with a curated dataset of financial documents, invoices, contracts, tax forms, and special reports to check the validity of our approach. Precision, recall, and F1-score stand as the means of our models' comparison on equal footing with more conventional benchmarks. The findings show that there is a great improvement in the classification precision, i.e., performance of the classifications, especially on the complicated multi-format documents and those with structural abnormalities. The proposed study will add to the existing literature on intelligent document processing (IDP), as this study will establish the effectiveness and performance of transformer-based models in a workflow-scale endeavor. We end by discussing the research which should be conducted in the future, such as cross-lingual adaptation, integration with retrieval-based systems and application in document-intensive regulatory settings

Keywords: Transformer Models, Financial Document Classification, BERT, LayoutLM, Enterprise Automation, Hybrid Workflows.

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