When was the last time you or anyone on your property tax team went home at 5 p.m. on a workday during tax season?
Sounds impossible, right? Well, that was the case with one of our clients, whose office I stopped into recently at the end of a workday. The place was deserted. When I asked where everyone was, the response was, “They went home. They’re done!”
That small miracle could be attributed to technological advancements in robotic process automation (RPA), machine learning, and artificial intelligence (AI)—and the team’s wise decision to embrace these concepts and apply them to their own tax work. And you can’t afford to miss out—PWC’s artificial intelligence report attributes a $6.6 trillion increase in productivity across all sectors to AI through 2030. In this article, you’ll learn how tax teams of all kinds are already using AI and other advanced technologies to be more productive—and how you can be, too.
RPA, Machine Learning, & AI Defined
There are definite distinctions between these technologies, though sometimes the lines get blurred.
Robotic process automation (RPA) is an application that performs highly logical automated tasks. These types of tasks can be easily performed if there are clear conditions associated with carrying out the task; for example, “If this is true, do this. If this is false, do that.” RPA can overlay a legacy application as described above; it may also be embedded within an application, if that application is advanced enough.
Machine learning refers to the science around teaching computers to progressively improve their performance on a task. In contrast with RPA, which is logical and condition-oriented, machine learning requires the computer to have some degree of cognitive capability. The computer must be trained to detect data patterns or relationships that will then help it to draw conclusions.
Artificial intelligence (AI) refers to computer systems that can perform human-like tasks. There’s no teaching component involved here; it’s about creating a neural network that intakes large quantities of data and, on its own, builds algorithms that help it determine the right way to perform a task.
8 Ways These Concepts Are Being Applied To The Tax Industry
1. RPA can automate repetitive tasks.
Many tax professionals perform the same tasks repeatedly. They constantly click the same buttons to generate reports, for instance, or follow the same series of actions to retrieve a list of assets each time one is needed. But thanks to RPA, manual, repetitive tasks like these can be automated.
Using this type of tax robotics, you can program your computer to go to certain websites or legacy applications (like Oracle) to run tasks. For example, if you traditionally input account numbers or asset ID numbers onto a spreadsheet then run a report with particular filter criteria, you can automate the process so those numbers are filled in ahead of time in a grid. RPA will then mimic your actions of clicking on buttons and setting up filters, and generate the report for you.
Robotic process automation in accounting is also becoming increasingly common for things like invoice payment (automatically locating, zipping, and uploading files); accounting reconciliation (bridging various data sources to compare invoice discrepancies); and financial closeouts and reporting (automatically processing tax entries into Quickbooks from a spreadsheet, for example).
Want to learn how CrowdReason software uses embedded RPA and other advanced automation techniques? Schedule a free demo to see it in action.
2. Machine learning can extract key data from tax documents.
Tax teams spend a lot of time on three foundational activities: classification, taxonomy, and data extraction. Machine learning can accelerate the process of analyzing tax documentation—identifying the sender and defining the useful information—and inputting the relevant data into a software system. (Tweet this!)
CrowdReason’s MetaTaskerPT software uses machine learning algorithms and crowdsourcing to help the computer system get answers to questions that would otherwise require people to make those determinations:
- Classify documents. MetaTaskerPT poses questions to on-demand labor platform workers, asking them to identify the document: What is the document type? Who is the collector for this tax bill? Who is the assessor for this assessment notice? An answer is deemed correct if it is produced by three separate people. (This tactic also improves the accuracy of the data collection as opposed to relying on just one team member to get it right.) This classification process helps determine the type of document being extracted.
- Define the taxonomy of the document. Again, MetaTaskerPT poses several questions to the crowdsource workers, including: Is there an account number? How many payments are on this tax bill? Is there a discount available on this tax bill? The taxonomy process defines what is on this type of document. Once we have a consensus in triplicate, we move on to the third stage.
- Extract the required data from the document. MetaTaskerPT now understands what the document is and what’s on it, so the software can ask those same crowdsource workers for the specific information needed.
Over time, MetaTaskerPT begins to recognize documents and is able to replace those human data processes with machine-generated answers.
3. Machine learning can help classify tax-sensitive transactions.
Tax practitioners typically perform classification activities manually, which takes up valuable time. For example, machine learning tax algorithms can be developed to search for and identify assets that are incorrectly booked into certain accounts by an organization’s finance team, based on historical classifications your team has made. Applying machine learning to manual tax classification processes could reduce the number of manual reviews significantly—from as many as 50,000 transactions yearly to less than 300.
4. Machine learning can be used to analyze notices from tax regulators.
These standard-format notices are either informational or require some type of action on your part; currently there’s no way to know without a manual review. Machine learning in this area of tax performs an initial review of incoming notices, flagging the ones that need attention.
5. Artificial intelligence in auditing can be used to identify potential tax fraud cases.
Referred to as “predictive modeling,” machine learning applications are now being used by tax agencies to identify cases having characteristics that could indicate potential fraud. It often helps find subtle clues hidden in mounds of data that are sometimes missed or overlooked by auditors.
6. Artificial intelligence can help identify possible deductions and tax credits.
The enormity of the Internal Revenue Code (the “tax code”) and the complexity of the rules themselves make it a challenge for any organization to stay compliant, much less reduce their tax liabilities. Therefore, artificial intelligence is well suited for tasks that require a deep analysis of the code. H&R Block uses IBM’s Watson AI system to power its tax questionnaire. Using years of previous tax documentation as a foundation for learning, this AI application also has an in-depth understanding of the tax code and stays on top of yearly changes. As a result, it’s easier for tax practitioners to identify key areas for possible savings.
7. RPA and artificial intelligence can be used to compare pricing structures for more accurate transfer pricing.
For organizations that operate internationally, transfer pricing involves comparing various pricing structures and identifying similar transactions for purposes of ensuring fairness in global operations. Deloitte is using artificial intelligence to simplify that comparison process. Rather than having members of the tax team manually search databases for companies that operate in a similar manner, with similar pricing structures, an RPA application can automate that task. They are also working on using AI tax software to estimate the extent to which transactions are similar, which will save additional time.
8. Artificial intelligence can make tax forecasting more accurate.
AI can elevate the method of tax forecasting from simplistic modeling techniques (such as linear interpolations or basic regressions) to advanced predictive analytics. For instance, algorithms could analyze corporate and seasonal data help detect trends within various tax filing cycles—an annual, quarterly, monthly, or even more frequent basis. Those trends could then be used as the basis for predicting what’s likely to happen next. Even weather data could be incorporated into the analysis, helping to better forecast sales and tax burdens.
Elevate Your Property Tax Team’s Value With Robotic Process Automation & Machine Learning
Filing returns, tracking notices, processing tax bills—automating all these things greatly simplifies your ability to comply (and keep up) with jurisdictional requirements, making your job easier.
And yet the value of these technologies goes beyond simply making things easier. Being bogged down by data entry and information tracking keeps highly-trained tax professionals from utilizing their skills to the fullest extent. In our experience, organizations get greater value and financial return from a tax team that spends more time on strategic knowledge work instead of administrative tasks; tax professionals also appreciate the fact that they can contribute to the bottom line in a more substantial way. For example, they are better tasked with answering questions such as: Is our business property overassessed? If so, how can we get it reduced? What data do we need to collect and present to do that?
At CrowdReason, our belief is that much—if not eventually almost all—of tax compliance can be automated. We’re passionate about helping tax professionals do the job they were meant to do—and the one that will benefit their organizations the most financially. That’s why we found a way to incorporate RPA and machine learning into our property tax software.
If you’re interested in learning more about CrowdReason property tax software, including MetaTaskerPT for data extraction and TotalPropertyTax (TPT) for tax management, contact us. Or, request a demo to see it in action. We’d love to discuss your team’s unique needs for property tax software and how we can help.