FREMONT, CA: The finance sector is one of the industries which have successfully implemented digital transformations. The organizations in the industry have found a range of applications and the tax function is no exception. While the regulatory authorities struggle with discrepancies in pre-configured algorithms, and the organizations are fighting compliance issues that can often come across as ambiguous, the challenges of adopting technology in the taxation field can be addressed, with the help of data analytics and tax specific technologies.
Robotic Process Automation (RPA) and Artificial Intelligence (AI) have successfully altered tax data flow and processes. Manually entering invoices into the ERP is now a thing of the past as they are being replaced by QR coded invoices which need to be scanned or read using Optical Character Recognition (OCR). Through a Machine Learning (ML) model, organizations can automate the invoice accounting to determine taxes.
Another challenge associated with regular tax exercise is its periodic nature. Through a continuous monitoring of performance and repeated interventions, tax planning and projections do not have to remain cumbersome. This would mean a tax model with more frequency. Organizations will hence be able to determine the impact of various interventions.
The availability of records of taxes for analysis can help the tax-specific technology to help build a model with better planning and simulation. This can be done by managing tax returns along with managing both upstream and downstream processes associated with the tax returns. The data collected from multiple returns will equip the system to improve its interactive analytics. The method of data analytics should be carefully planned and executed accurately to make the most of it.
Taxation can soon advance to the next level of digitalization provided the finance organizations are ready to work their way towards a secure model of taxation and reduced regulatory discrepancies.