Treffer: Research on Tax Risk Identification and Assessment System Assisted by Software and Deep Learning.

Tax risk management is crucial for businesses to ensure compliance and minimize financial risks. The challenges arise from the inaccuracy of historical tax data in predicting all potential risk scenarios. The objective of the research is to improve accuracy and efficiency in detecting tax-related risks by leveraging advanced deep learning (DL) and software integration in real-world scenarios. Data for the research are gathered from various sources, including historical tax records, transaction data, and compliance reports. Data preparation includes cleaning the raw data by handling missing values, correcting inconsistencies, and normalizing the data to normalize ranges. Linear discriminant analysis is used for feature extraction, reducing dimensionality while preserving discriminative data. The research offers a novel DL approach named frilled lizard optimizer-driven intelligent gated-long short-term memory (FLO-IG-LSTM) to enhance the identification and assessment of tax risks, ensuring more reliable and automatic predictions for tax professionals and businesses. The system is implemented in Python, and the results establish the representation’s capability to identify and assess tax risks effectively, outperforming existing methods in efficiency and accuracy. The FLO-IG-LSTM model achieved an accuracy of 97%, an execution time of 2.3ms per record, a latency of 1.8ms, an F1-score of 94%, a precision of 95%, a recall of 92%, and an error attribution accuracy of 89.7%. The confusion matrix revealed 1450 true positives, 1320 true negatives, 50 false positives, and 45 false negatives, highlighting a well-performing classification model with minimal errors. It offers a capable method for automating tax risk management, though additional improvements are needed to improve its scalability and flexibility across various tax systems. [ABSTRACT FROM AUTHOR]

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