Artificial intelligence (AI) and machine learning (ML) are growing faster than anyone had predicted, infiltrating nearly every industry including tax preparation services. Tax submission tools can already deduce the likelihood of an audit based on previous tax filing history and averages among similar businesses. With predictive tax analytics, CPAs and other professionals can utilize the advances in AI to look at algorithms and forecast potential tax liability for the future.
ML for Predictive Tax Analytics
As tax laws continue to grow more complex, corporations and entrepreneurs are required to keep meticulous records of their income and expenses. In a recent study by Deloitte, 60% of company leaders indicated they were already using or planned to use data analytics for better tax compliance and strategy.
ML technologies are revolutionizing tax analytics by improving accuracy, processing complex data more quickly and adapting to new details as they emerge, helping companies comply with tax laws. Data that would take a CPA days to sort through and analyze siphons through computer at lightning speed. For example, machines can sort through large datasets and predict how much a company might owe at the end of a quarter so it can make payments timely and accurately.
Benefits of Deploying ML in Tax Functions
AI algorithms look at past factors to predict future tax liabilities. Some of the data a machine might sort through includes past tax filings, current fiscal year transactions, updated tax codes and required paperwork, market trends for the industry and past patterns for estimated tax payments.
Tapping into the power of predictive tax analytics can reduce falling under an audit or the risks of being accused of fraud.
Other benefits of using ML for tax analysis include:
Automating Repetitive Work
Much of the work for tax compliance involves repetitive items you must cross-reference over and over. Machines can do much of this grunt work and free up your time to locate significant anomalies or improve processes. For example, computers can reconcile data and notify company accountants of crucial filing deadlines.
Predicting Tax Liabilities
Most companies must pay taxes quarterly, but the time-consuming task of adding up receivables and subtracting expenses can be draining on smaller businesses. Having a firm grasp of how much to pay in estimated taxes can prevent underpayment and penalties.
Keeping Pace with the Competition
Machine learning can transform a simple excel spreadsheet into something more definitive that helps a company advance into the modern age. Around 21% of North American companies are using ML smart tools to better analyze nearly every informational aspect of business operations.
Ensuring Tax Compliance
Human error can lead to miscalculations and missed deadlines. Machines can double-check computations and ensure spreadsheet entries match other accounts. If someone makes an incorrect entry, the computer will flag it so it can be fixed before taxes get filed.
Optimizing Tax-Saving Strategies
Machine can run case scenarios to determine the biggest benefits of making a purchase or taking cost-saving measures. Company leaders can see at a glance whether they should invest in new equipment, pay employee profit shares or set more aside for tax payments.
ML can also see if the business is scaling rapidly and make adjustments on the fly. It can even be trained to allocate funds for taxes, setting aside what’s needed to make payments in a timely manner.
Concerns over Ethics and Data Privacy
One concern however many have with using ML in tax analytics is data privacy for the company and employees. Companies can reduce these worries by being transparent in the decisions they make and how they utilize AI algorithms. To keep data secure, they must implement security protocols to protect assets.
Another thing to be aware of is that ML algorithms can show bias if not programmed correctly. It’s best to have several programmers to act as checks against one another. Predictions should be neutral, and company leaders must understand they aren’t always infallible.
