Organisations facing challenges in VAT data management
Frequently changing country-by-country reporting rules have added to the complexity. For instance, some of the EU tax authorities currently require invoice clearing i.e. real-time reporting of transactions even before the invoice is sent to the customer. Such a mandate for invoice clearance places an additional pressure on the businesses to ensure the data collected at the source is accurate before it is transmitted to the tax authorities.
Coping with these complexities without technological intervention is no longer sustainable for the companies having widespread operations. Before the introduction of real-time reporting requirements, the tax teams had an opportunity to look at the data and correct it but now they don’t have that time anymore. The discrepancies in reporting the data to tax authorities can lead to exposure to increased scrutiny and fines.
For the companies using the industry standard ERP systems, a shock comes with the realization that they aren’t always equipped to manage the complexities out of the box and often leave the role of providing up-to-date changes on tax legislation to either the customers or third-party solution providers.
Building an in-house automation system also does not diminish the challenge of keeping the multiple financial systems for ERP, procurement, etc. updated with the frequent changes in the rules and rates in the EU for VAT purposes. Rather companies operating in the EU may implement a specialized solution to significantly streamline their VAT compliance processes across the region by relying on specialty providers to adjust quickly to changes in tax legislation. The burden and costs of keeping VAT rules updated within the tax engine in such case is spread across their customer base.
Another challenge being faced by the companies is about VAT data management in particular. There could be various possible reasons for this such as different source systems, nature of operations, and different regions but the nature of the tax itself remains a central issue. The data required to prepare VAT returns is completely different from the data found in the financial statements, and thus the businesses will not simply stumble upon the relevant VAT data in their accounting systems.
To overcome these risks and challenges organizations have started centralizing compliance, using emerging technologies that will help to aggregate, validate, and report for compliance purposes, while using the analytics on the data gathered to help identify irregularities and mitigate risk.
Managing a large volume of data on different systems and compiling them together for returns comes with complicated processes making it difficult for organizations to deal with. Organizations have started partnering on building a system or solutions with the help of RPA, AI & ML to help with data complexities.
Artificial Intelligence (AI), Automation & Compliance
AI is a very broad term, which comprises of many components. Some of the most known ones are cognitive and machine learning like Siri & Cortana and one of the simplest ones could be the spell-check & grammar-check we use in our day-to-day life. Over the years AI has made a lot of progress in areas like pattern recognition, data recognition, and much more. With AI you can automate business processes and increase business efficiency.
Robotic Process Automation (RPA) is becoming the most popular technology among organizations that must deal with large volumes of data from multiple systems and multiple regions. Data complexities increase with the type of data captured in different systems. Managing this data is not easy and neither fun, but with RPA makes it easy to manage multiple data items and store it in the right way for VAT returns.
Automation in Data Management
Using automation for large data storage makes tasks such as data cleaning, standardization, data disputing, and creation or updating of metadata more successful. All these tasks are repetitive and unique. Each data access situation demands special considerations. This provides an ideal opportunity for applying for RPA. To manage complex data RPA can be combined with different techniques, for e.g., extracting information from OCR documents to create metadata. Other benefits of using RPA for large data sets are:
- Auto input data, replace manual file submission
- Migrating data between different systems
- Data combination, for example, combination to provide high-quality data sources
- Evaluating for anomalies and further developing information quality through repetitive assignments
- Empowering information and authentication of manual processes
RPA also provides a history of the changes it undertakes. It can be exceptionally significant for process optimization, regulatory compliance, and maintaining transparency in a complex data ecosystem. RPA is now moving to analytics in the cloud, specifically in retail applications, but its potential in deciphering data management challenges is of particular interest. It can be a compelling tool for sustaining data quality in the complex arena of AI. The power to analyze the RPA records can help an enterprise enhance its data quality and data extraction to ascertain where productivity is lost or where processes are performing below expectations.
