Machine Learning for Identifying and Classifying Uncertain Tax Positions in Corporate Filings

Abstract

Uncertain tax positions (UTPs) present significant challenges for both corporate entities and regulatory bodies due to their complexity, subjectivity, and the lack of standardized disclosure practices. With the growing volume and sophistication of corporate financial reports, traditional audit and compliance methods struggle to efficiently detect and classify UTPs. This study proposes a machine learning-based approach to identify and classify UTPs by analyzing linguistic patterns, contextual cues, and financial metrics in corporate filings, particularly in Form 10-K disclosures. Leveraging natural language processing (NLP) and supervised learning algorithms, we develop a predictive framework capable of flagging potential UTPs with high accuracy and interpretability. The model is trained and validated on a labeled dataset of SEC filings with annotated tax disclosures. Our results demonstrate the effectiveness of ensemble learning and deep learning models in automating UTP identification and offer insights into the key features contributing to model predictions. This research aims to assist auditors, tax professionals, and regulators by enhancing transparency and enabling more proactive tax risk management.