Information Extraction and Anomaly Detection from Turkish Financial Reports' Footnotes

Abstract

Financial statement footnotes contain critical information that complements numerical data; however, their text-heavy nature often leads to them being overlooked. This study aims to extract information from the financial footnotes of publicly traded companies listed on Borsa Istanbul to support financial analysis processes and detect anomalies. Within the study, 10 different questions were answered - five using natural language processing-based question-answering models and five using machine learning techniques. Various techniques were applied to analyze the consistency of financial data and detect anomalies, but challenges such as OCR errors, table structure loss, and API costs were encountered. The results demonstrate that NLP and ML methods are effective in financial text analysis and contribute to risk assessments by identifying anomalies. © 2025 Elsevier B.V., All rights reserved.