Enhancing Tax Revenue Forecasting Using CapsNet and CNN In Cryptocurrency-Driven Economics

Abstract

The modern concept of taxation has been accelerated by the emergence of internet-based economy and the use of cryptocurrencies. This shift raises various difficulties for tax authorities in terms of revenue estimates; it requires sophisticated methods for quantitative analysis of intricate economic trends. Previous works have mainly employed conventional ML methods which are weak in their ability to recognize dependencies in the features for data with high dimensionality, hence poor forecast precision. To counter these drawbacks, the use of CapsNets with CNNs, which forms a combined model contributing to better predictive capability is introduced. CapsNets ability to keep ownership of spatial hierarchies and sophisticated features of the input makes the method to accurately extract and analyze features from digital transaction data. Working in Python, the suggested model was compared to several traditional algorithms with which it demonstrated the highest accuracy rate at 99.1%. As concluded from the evaluation, the CapsNet-CNN model is not only resistant to the formulation of new structures, but also flexible to the fluidity of the digital economy environment. This work demonstates the promise of using modern DL methodologies for improving the accuracy of the tax revenues and provides insights for the policy makers who are keen on evoking sensitive and dynamic response strategies of the taxation authorities in a world where more and more of the economic operations are being carried out in the cyberspace. Future research will quantify the components in the model and expand the investigation to other industries and locations.