AI-Powered Tax Compliance: Enhancing Accuracy and Efficiency Through Predictive Modeling

Abstract

As compliance requirements are increasing internationally, organizations are under more scrutiny and pressure to ensure the accuracy and timeliness of their reporting obligations. Therefore, through a proposed solution, this white paper discusses how the compliance process can be made more efficient and cost-effective for commercial organizations amidst a myriad of compliance complexities by employing artificial intelligence (AI) as part of the solution. With the increasing use of AI and emerging techniques like machine learning, many avenues arise for further development due to the vast potential of effective predictive modeling. The growing sophistication of compliance obligations and business processes around the world has increased the expectations of tax authorities, promoting increased scrutiny over reported tax and assurance processes. This has led to a rapid and dramatic rise in the volume of compliance across the world, with pressures increasing drastically to ensure accuracy prima facie but also that sufficient interpretations are made in time and deemed defendable. As a consequence, organizations are increasingly facing the challenge of compliance in order to mitigate excessive liability, reputational risk, or unnecessary cash outflows.

In the game of compliance, those with the most limited resources are usually dealt the worst cards. Medium organizations are caught between an increasing complexity of tax compliance across jurisdictions and business processes, and revenue authorities leverage technological advances to increase scrutiny of compliance defenses. This white paper discusses how the further use of AI has the potential to dramatically reduce the cost of preparing compliance, despite the limitations of robustness and interpretability. In particular, the usage of an ML algorithm for both ingestion of compliance inputs from various unstandardized data pools and outputs and extracting predictions on them by means of textual pattern-recognition is discussed in detail. This paper is concluded by outlining how further development on this technique could make a significant dent in costlier, more burdensome compliance obligations while increasing robustness and expelling trivialistic outputs. AI has the potential to extract coherent and legally defensible implicit data from massive tax compliance checks across jurisdictions, although the output often requires extensive human verification.