Machine learning is changing how accountants do their jobs and the tasks they need to handle. Here’s what you need to know about this technology and how you can use machine learning for accounting.
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To give you a thorough understanding of why accountants must embrace machine learning, we’ll talk about:
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Before we proceed, let’s make things clear first:
Machine learning is not a threat to the accounting profession.
Since time immemorial, the use of tools has always been a part of every accountant’s job. Our never-ending quest for convenience has led us to the invention of the most useful tools we know to date. Just think of the good ‘ol abacus, calculator, Excel, and your favorite accounting software.
What is clear is with each invention, there is one constant element: humans.
Without humans who find these tools useful, who are knowledgeable of using them, and who actually use them, these tools won’t last long.
In other words, it’s not about a question of whether machine learning and other artificial intelligence (AI) tools can do your job right now. Instead, it’s about you keeping up with these innovations so you can offer more value to the clients you’re working with.
Machine learning, by itself, cannot replace human judgment and critical thinking skills. When used with caution, it can be a good addition to your workflow due to its ability to pull out information, find complex data patterns, and make predictive choices. In other words, artificial intelligence, when paired with human intelligence, will help the latter work smarter.
Understanding what machine learning is, its accounting applications, limitations, and the skills you need for it are the best ways to take full advantage of this technology.
Let’s start with the basics.
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What is machine learning and how does it work?
Machine learning (ML) is a computer program’s ability to learn new data and perform complex tasks without explicit programming. As a branch of artificial intelligence (AI), it’s capable of imitating intelligent human behavior and performing tasks autonomously.
If it sounds too technical, let’s simplify its explanation using this scenario.
Imagine that you’re a fresh graduate who started an entry-level corporate job. Despite having formal education, you still need to learn the ropes of your new role. Think of various industry regulations, corporate lingos, communication styles, business tools, and standardized company protocols. Taking it all in can get overwhelming and you’re likely to make mistakes along the way.
At the beginning, an experienced employee will need to train you. Through this, you can keep up with your tenured colleagues and ensure that you’re doing your job correctly. With each day that you absorb new information and become more exposed to your responsibilities, you’ll become more independent at doing your job.
Machine learning also works this way.
Like a newly hired employee learning about how the company works, ML relies on existing data patterns (i.e., the knowledge of an experienced employee) before it can figure out and accurately provide the information the user needs (i.e., complete the tasks independently).
Depending on the data it consumes — including the way it’s modeled, trained, and tuned — machine learning can perform 3 main functions:
- Make forecasts (predictive).
- Suggest solutions (prescriptive).
- Provide possible reasons why certain things happened (descriptive).
Incorporating these ML functions into accounting software and business intelligence tools can result in streamlined accounting processes. Through its proper use, you’ll no longer spend long hours doing manual tasks. You can instead shift your focus to higher order duties, which can be more beneficial to your long-term career trajectory.
How can accountants use machine learning for accounting
Accountants can use machine learning in different areas of accounting, specifically in automating routine tasks. It can be through ordinary and everyday work like accounts reconciliation and expense management or with more complex areas like tax compliance and advisory.
“Automation is an opportunity to redesign the process and leverage the intellectual capital you’ve got for the future,” said Professor Michael Davern FCPA via an article published by CPA Australia.
Davern, the chair of accounting and business information systems at the University of Melbourne, also added that “it’s about getting staff familiar with the technology, the process and what the data means. That’s a mindset change for some people.”
So what can this technology do for you and how can it help you take on more value-added projects? Let’s start with its practical applications in specific areas of accounting.
1. Recurring accounting tasks
If you’re using Xero, Power BI, and other accounting software and business intelligence tools, then that means you’ve already had a fair share of experience leveraging the capabilities of machine learning.
Xero, for instance, uses machine learning techniques for its most popular features like bank reconciliation predictions, automatic form filing in Hubdoc and Xero Expenses, and its paid add-on planning/forecasting tool called Analytics Plus.
Meanwhile, PowerBI’s Automated Machine Learning (AutoML) lets you build ML models even if you don’t have prior experience doing such.
Using this tool, you can generate your own machine learning model which you can train using your company’s unique historical data. It can then be applied to various ML tasks such as regression, classification, forecasting, and computer vision. In simple terms, these tasks are equal to the ability of making sales forecasts, fraud detection, prediction of future trends, and image classification.
Other recurring tasks that you can automate using ML include creating expense reports, managing accounts payable and receivable, data entry, invoice processing, expense categorization, and other repetitive tasks.
Read Next: Accounting Process Automation: How to Automate Accounting
2. Tax compliance and planning
Here’s the deal: The Internal Revenue Service (IRS) is already utilizing machine learning in its tax enforcement efforts.
In 2017, the IRS adopted ML techniques to identify tax fraud and noncompliance, specifically the detection of questionable refunds on individual tax returns.
The government agency has since then continued its increasing use of ML to close tax gaps. In fact, the IRS announced that they have collected more than $482 million from the taxes owed by 1,600 millionaires. Several cases were also filed against individuals and entities that committed tax crimes — all of which were made possible using cutting-edge machine learning technology.
The question is, how do you stay compliant on top of IRS’ heightened tax enforcement efforts?
A possible solution: use machine learning to keep up with the machine learning-powered initiatives of IRS.
Here are some ways ML can help you with tax compliance:
- Predict deductions that the IRS may possibly dispute
- Check if an entity qualifies on claiming deductions according to all applicable regulations
- Monitor changes in legislations and identify related implications and obligations to the business
- Automatically track and record tax activities and outcomes to create well-documented audit trails
Streamlining your recurring accounting and bookkeeping tasks, as we have discussed above, also contributes to improving your tax compliance. After all, maintaining clean books is essential in filing accurate tax returns.
3. Audit
Machine learning’s ability to analyze vast amounts of data and reveal complex data patterns is essentially its most notable feature for auditors.
A KPMG point of view article explains how ML works in audit this way:
“Machine learning techniques where, through complex algorithms, the technology can scan information, model it against thousands of assumptions drawn from external scenarios and highlight risks and insights. This predictive analytics is a step towards deep learning where, in the future, the application will be able to ‘think’ for itself, learn from the results and run more scenarios and tests accordingly.”
With the way it works, this technology offers real-life applications in audit. Among its current uses include:
4. Advisory and consulting
Executive-level accountants who offer advisory and consulting services can provide more value to clients when they integrate machine learning into their work processes.
Given everything that we’ve discussed so far, ML’s ability to process large datasets can help you predict trends, prepare the necessary budget and cash flow projections, and help your client make smarter financial decisions.
Impacts of machine learning on different accounting roles
Machine learning has different applications in various accounting areas — and so do its direct effects on different accounting roles. Here’s an overview of how accounting professionals can be affected by this modern technology.
1. Management accountants have a huge role in developing proper governance and internal controls
One of the downsides of machine learning is biased data. This means this technology has the tendency to provide analyses that can leave a negative impact on the organization.
Because of this, companies with organization-wide AI initiatives should work with management accountants to mitigate risks caused by machine learning.
Management accountants can assist or oversee the development of proper governance and internal controls. The automation capabilities of machine learning can also help these accountants focus on more complex tasks such as analysis and advice.
2. Tax accountants will still be in high demand to deal with complex tax regulations
Tax accountants will experience lesser impact compared to other roles because of companies’ need for specialized advice and tax planning services.
Tax laws and other related regulations — be it local or international — are still way too complicated for machine learning. Thus, companies are still heavily reliant on tax accountants’ ability to conduct technical research and assess how certain tax rules apply to their business.
3. Auditors need to step up to remain competitive
As AI, in general, takes over the majority of analysis functions, entities may need fewer audit staff.
Despite this, auditors will play a bigger and more value-adding responsibility amid the continuous integration of AI in the accounting industry.
This is supported by 2024 KPMG research that surveyed senior executives and business leaders across 1,800 companies worldwide. The research revealed that 64% of companies expect auditors to have a role in evaluating their use of AI in financial reporting, providing assurance and attestation over their AI controls.
In the same research, Matt Campbell, the Chief Technology Officer of Audit KPMG in the UK explained,
“Businesses are looking to their auditors to lead the AI transformation due to their deep understanding of financial reporting processes and ability to identify areas where AI can add the most value.”
This means that on top of their traditional accounting and audit knowledge, auditors must also sharpen their understanding of generative AI. At the same time, they should also focus on improving their technology skills.
Benefits and limitations of machine learning
Machine learning is highly beneficial for your accounting practice. However, it also has its limitations given that the results it yields also depend on the data you feed and the way you train the model.
a. Benefits
An efficient accounting process is one of the most obvious benefits of machine learning. Its ability to process large volumes of data saves you time and effort doing repetitive and predictable accounting tasks.
Moreover, businesses undergoing digital transformation can also expect positive returns. As the discussion paper from the McKinsey Global Institute stated, early adopters from the financial services sector with proactive AI strategies have higher profit margins compared to their counterparts.
And while machine learning is already a part of the new reality, it will continue to be an essential part of the finance and accounting profession in the future.
Thus, if you aim to reach greater heights in your career, learning the ins and outs of machine learning can give you a huge competitive advantage. Rather than dealing with transactional and repetitive tasks, understanding machine learning gives you the chance to work on value-adding (and higher-paying) tasks.
Related: Accounting Process Improvement: How to Reduce Inefficiencies
b. Limitations
Machine learning may have the ability to learn like humans do, but it has not attained the full capabilities of a human mind yet. In fact, its inability to think critically and make sound judgments can affect the insights it provides.
Its high reliance on the data it’s being trained from makes it susceptible to drawing inaccurate patterns, trends, and relationships. This is especially true when it deals with poor or small quantities of data.
Because humans train ML systems, their biases may be incorporated into algorithms. Again, ML is reliant on the data it consumes so it’s highly likely to replicate forms of prejudices.
The adoption of AI and machine learning may also take some time. Organizations belonging to the accounting and finance industries will need enough time, resources, and expertise to fully adapt the use of machine learning and AI systems into their processes. Cost is also a major factor, of course, along with the tight competition for top digital accountants.
Use technology to your advantage
Do you need assistance with your digital transformation initiatives? If you do, consider working with D&V Philippines.
As a specialized accounting outsourcing company, D&V Philippines heavily invests in the training and development of its people. This way, we can ensure that they have extensive knowledge of using cloud accounting solutions, business intelligence and data analytics software, as well as the necessary critical thinking skills and knowledge in using these modern tools.
If you need more staff like them in your firm, feel free to get in touch with us or download our whitepaper, The Rising Frontier: Harnessing the Power of Business Analytics, to learn more about our AI-powered accounting solutions.
This article was first published on 6 August 2021 and has been updated since then for relevancy and comprehensiveness.
Edited by: Mary Milorrie Campos
Last updated on: 27 February 2025
