Automating indirect state and local tax compliance process.

• Author: Din, Josh

With continuous change in global business environments and ever-evolving global tax regulations, tax departments are asked to do more with less and comply with new reporting requirements employing older bolt-on solutions and spreadsheets that manually aggregate data to facilitate calculations.

Indirect tax departments are prime examples of how data is pulled from various sources to feed downstream tax applications, revealing trends, patterns, and associations in the data that affect the tax function within an organization. As a result, indirect tax departments spend more than 80% of their time performing low-value and repetitive tasks to comply with their monthly filing requirements and fall short of analyzing data, which leads to a higher risk of penalties in the future. There are many hurdles in blending unstructured data from various sources in spreadsheets and getting that data into a standardized format to feed into downstream tax applications to complete monthly compliance obligations.

This is where the concept of tax data automation comes into play, by using extract, transform, and load (ETL) tools available within organizations to provide governance and control, risk management, process improvement, transparency, and adaptability to continuously evolving regulatory requirements. In addition, this can enable organizations to do data analytics in the back end to drive insights and identify anomalies that can significantly affect their operations.

To reap the benefits of automating processes that are managed manually, companies should design and adopt a well-organized systematic approach that can identify candidates ripe for automation to streamline the indirect compliance process with configurable tools along with powerful data reconciliation, adjustment, and reporting capabilities. This approach recognizes all the monthly reporting and compliance requirements related to indirect taxes and consists of the following steps:

Identify automation candidates: Companies should start by thoroughly assessing the current process and identifying calculations that recur versus one-offs, bifurcating source data to identify whether it is structured or unstructured, and capturing all the calculation requirements to determine whether they are objective or subjective. Calculations that are recurring, use structured data from source systems, and apply objective calculation logic are prime candidates for...