Anomaly Detection in Tax Filing Documents Using Natural Language Processing Techniques

This paper introduces a novel approach to tax fraud detection utilizing natural language processing techniques for identifying anomalies in tax filing documents. The methodology integrates tax-domain specific BERT embeddings with bidirectional LSTM networks to capture contextual relationships within tax documents that traditional numerical analysis might overlook. We present a multi-component ensemble framework that processes both structured and unstructured components of tax filings, extracting semantic relationships between financial entities while maintaining sensitivity to numerical inconsistencies. Using a dataset of 15,000 tax documents with 8.5% labeled anomalies, our approach demonstrates superior performance compared to existing methods, achieving an F1-score of 0.868 and AUC of 0.931a 7.6% improvement over state-of-the-art techniques. The framework exhibits varying effectiveness across document types, with higher detection accuracy for individual income tax returns (F1-score 0.889) compared to business tax declarations (F1-score 0.818). Performance analysis reveals that semantic relationship features contribute significantly to anomaly detection in business tax documents, while numerical consistency features dominate in individual returns. Despite computational requirements exceeding traditional methods, the enhanced detection capabilities address critical gaps in existing tax fraud detection systems, particularly for sophisticated evasion strategies that manipulate textual elements while maintaining numerical plausibility.