Attributed to Eric Lefebvre, Chief Technology Officer at Sovos
For organisations looking to navigate the fast-paced changes in regulatory environments, AI offers a solution applicable across various business sectors. Industries handling extensive data, such as tax compliance, are beginning to recognise the potential of machine learning and intelligent automation.
Generative AI represents an advancement that enables everyone from CFOs to frontline compliance managers to address use cases more swiftly using readily available tools. It can enhance the efficiency of internal processes, which is particularly beneficial for smaller organisations. However, the adoption of AI and its effects will be more gradual and evolutionary rather than immediate and revolutionary.
Business leaders should remember that we are at the beginning point when it comes to using this technology in business. There are tremendous benefits, but there are also real risks, such as the potential leak of intellectual property used in AI training. This amount of computing power has never been easy to implement, and it’s also easy to get wrong. Leaders undertaking an AI implementation should keep the adage in mind that with great power comes great responsibility.
With the right approach, there many use cases where AI can make a significant difference in compliance management.
Revolutionising Tax Tech & Regulatory Compliance
Tax compliance and regulatory reporting are ripe with opportunities for automation and AI implementation. For instance, in indirect tax reporting – such as for value added tax (VAT) and good and services tax (GST) – processes remain the same every month. AI can help organizations keep track of their tax obligations, flagging discrepancies as they come up, such as ensuring the right VAT is applied to a given transaction.
There are many similar process efficiencies. In purchasing, AI can facilitate decision-making based on factors such as who is entering an invoice. In spend management, AI can speed up time-consuming data entry by using optical character recognition (OCR) to ‘read’ documents, and natural language processing on financial document contents. AI assistance can also help employees understand spending policies, help managers ‘zoom in’ on anomalous spending patterns, and help controllers by suggesting classifications for transactions.
Worldwide, there are an estimated 14,000 regulatory changes each month across the approximately 19,000 tax jurisdictions. GenAI will be an invaluable tool for monitoring this massive number of tax content changes and be able to keep up with regulatory changes across internal compliance or audits. Over time, predictive models will even allow organisations to anticipate and get ahead of regulatory changes before they happen.
AI can be used to identify potential customers and geographies, driving top-line revenue while also helping bottom-line growth. For companies in the B2B space, AI fraud tools can cut detection times from days to minutes, meaning fraud detection can move from being reactive to happening in real-time. In the proof-of-concepts Sovos has piloted, we are already seeing accuracy up to 99%.
Successful Integration for the Finance Department
For companies looking to introduce AI into their finance functions, there are best practices that can help make the evolution a success. From the outset, departments must have a clear understanding of the problem they are trying to solve, the value proposition and how feasible it is. Those making the decision need to consider the potential impact AI will have on stakeholders such as customers, employees, partners, and regulators.
As with most technology, the value of the results of using AI is only as good as the data put into it – and selecting the right tool for the job. AI is not a one-size-fits-all solution. There are many different types, such as machine learning, natural language processing, computer vision and deep learning. Finance and IT should work together to evaluate the availability and quality of their data, the scalability and security of their infrastructure and the compatibility and interoperability of their systems. Furthermore, not all large language models (LLMs) are created equal, and some will perform a given use case better than others. This is a great example of pilot often, fail fast, and iterate.
Ensure employees gain confidence in any AI solution by introducing it gradually, providing a solid foundation through training and education in how it can be used. There are three stages to AI adoption: algorithms making suggestions, algorithms offering shortcuts, and finally, algorithms doing the work, while expert employees monitor their progress. At each stage, business leaders need to assess how the AI solution is working to ensure it offers the greatest benefit to the organisation.
Maintaining a Leading Edge in Compliance
Organisations that adopt innovative tax technology solutions incorporating AI will have a significant competitive edge. Generative AI relieves finance leaders of tedious, time-consuming tasks, enabling them to concentrate on strategic business activities such as mergers and acquisitions, and risk mitigation. This transforms compliance from a mere functional requirement into a genuine catalyst for growth.
