Automated Financial Report Detection, Classification and Structure Recognition Using YOLO and SLANet

Financial report/statement analysis is an essential phase in ensuring regulatory compliance and acts as a fundamental safeguard against financial misconduct. Organizations can discover irregularities, identify potential fraud tendencies, and maintain financial transparency by closely reviewing financial statements. However, in order to proceed with such a work, the statements must be extracted from annual reports and presented in spreadsheet format that allows for swift examination. Therefore, we built a scalable and automated pipeline based on using three state-of-the-art machine learning models: YOLO, PP-OCR, and SLANet, along with three financial table datasets. We introduced a novel classification layer to significantly cut processing time by removing non-statement tables present in the annual report. Our best model configuration achieved an mAP of 99.5%, a precision of 98.4% on financial table detection, and 100% accuracy on the classification of such tables, with an average per page prediction time of 662.6ms from PDF to Excel format. Additionally, SLANet demonstrated a TEDS value of 0.876 on statements of financial position, showcasing 17.6 times faster inference speed than the current benchmark. Our automated ML-based pipeline works as a precursor to financial report analysis while eliminating the need for manual labor and reducing time.