Enterprise Tax Assessment and Risk Avoidance Based on Deep Learning - Yali Lan (International Journal of Computational Intelligence Systems)
Abstract Business tax assessments ensure financial solidity and regulatory adherence. Inaccurate or late tax returns can harm an organization’s reputation and economic health. Conventional tax risk appraisal techniques usually fail to estimate taxable input risks and future fiscal burdens correctly and hence are left with ineffective mitigation of risks. The proposed method forecasts the possibilities of taxable inputs and their depreciation, which results in risks over different financial quarters. The cumulative taxable input risks are updated based on the previous inputs and their claimable part to improve the risk prediction. In this prediction process, deep learning is employed; this learning model is designed with two conditional layers. The first conditional layer is responsible for identifying the taxable inputs, and the second is responsible for determining the risks due to external input changes. These two factors are combined using the previous risk factor impact to verify their existence. Based on this existing factor, the number of assessments is increased or benchmarked for further audit. The topic model accurately predicts taxable input risk by financial quarters, continuously refining risk estimates based on the incorporation of prior data. Continuous learning and benchmarking enable the model to adapt to changing tax conditions. By incorporating deep learning into tax evaluation, businesses can enhance financial stability, streamline risk management, and facilitate improved compliance. The approach simplifies business complexity and allows for more precise tax planning.