Tax Management in the Digital Age: A TAB Algorithm-based Approach to Accurate Tax Prediction and Planning

Abstract

Machine learning models can then be employed to accurately forecast the likelihood of a taxpayer defaulting on their taxes. This can help tax authorities to better allocate resources and identify potential cases of tax fraud or evasion. It is very challenging to build an identification model in the area of taxes due to a large amount of unlabelled tax data, the cost of data annotation in a single place, and the differences in attribute distributions between regions. This research focuses on developing popular ML-based algorithms called Transfer Adoptive Boosting (TAB) for tax fault detection. This algorithm is especially useful for predicting tax compliance outcomes, as it can be used to identify the most important factors in predicting tax compliance. TAB can be used to create a tax management and prediction system that is optimized to accurately predict tax compliance outcomes. By using a combination of weak learners such as decision trees, logistic regression, or neural networks, the TAB algorithm can be used to create a highly accurate tax management and prediction system. The model can also be used to identify which factors are most important for predicting tax compliance, allowing for more efficient tax management. By using TAB, tax managers and policymakers can more accurately predict the outcomes of tax compliance, leading to more efficient and effective tax management.