Accountants Are Using Machine Learning to Boost Efficiency

Machine learning technology is highly important in the accounting sector, so accountants need to use it wisely.

machine learning in accounting
Shutterstock Photo License - Andrey_Popov

Machine learning technology is changing many sectors in tremendous ways. The accounting sector is no exception. Analysts from Markets and Markets project that the market for AI in the accounting industry will exceed $4.7 billion within the next two years.

A lot of accountants are discovering innovative ways to take advantage of the benefits of machine learning. They have found that AI technology can help boost efficiency, reduce errors and improve customer satisfaction.

Machine Learning is a Huge Boon to the Accounting Sector

Accountants are an innovative and successful bunch since there’s a lot more to the profession than just number crunching. However, working as an accountant in a company and running your own accounting firm are two very different roles. Experienced accountants do indeed have a better understanding of core business than most. Still, there are several other aspects of business management that they might not be trained or prepared to handle.

Consequently, this knowledge gap can affect the company’s efficiency unless the necessary steps are taken to prepare a counter-strategy. The good news is that you can reduce the issues that you will experience by taking advantage of machine learning technology. Smart accountants also recognize the need to leverage data science in their profession.


In the coming paragraphs, we will discuss a few tips for boosting efficiency in accounting firms with AI, as suggested by some of the most successful names in the sector.

Streamlining Workflow with Machine Learning

We are not saying that accountants are unaware of the importance of workflow management in business. Still, even the most qualified accountants are not always trained to be business leaders capable of implementing the necessary steps. The good news is that there are ways to improve workflows with AI.

As for what steps can be taken to maximize productivity and improve workflow management at an accounting with AI, consider the following tried and tested suggestions:

  • Identify all business processes (the work) and rank them in accordance with their necessity and value to the firm. Machine learning technology can improve workflows and help you assign a weight to the importance of different tasks. You might subjectively rank things in a certain order, but machine learning algorithms can be trained to tell how much value should be attributed to a certain function.
  • Define, designate, and delegate job roles for your workforce to handle the identified and ranked processes accordingly. Machine learning technology can help in this area by determining the skills different employees have in handling different tasks. It can accomplish this in a number of ways, such as reviewing past performance reports and error rates on certain projects to figure out which employees you should delegate to.
  • Provide employees with access to the appropriate AI-driven tools, so that they can become more productive with their time.. Many new software applications use AI, such as SaaS and specialized financial modeling tools. However, AI tools are only useful if you make sure employees have access to them.
  • Invest in a digital work map/project management software, so that both progress and bottlenecks always remain transparent. Digital work maps use AI technology to find the most efficient use of resources.

Use Machine Learning Strategically and Create Provisions to Manage and Mitigate the Impacts of Liability Lawsuits

The best way to minimize your firm’s chances of getting sued is never to make a single mistake. Although that is what every accounting firm should aspire to, it would be completely irrational to expect that for obvious reasons. A single mistake, a minor oversight, or sometimes, just plain bad luck can cause severe damage to a client’s finances.


Consequently, the client will hold you accountable for the mistake, which in turn can have severe negative impacts on your accounting company’s reputation, finances, and focus. The Hartford explains how accountants professional liability insurance can help accounting firms manage and nullify most of these negative impacts, even before they become serious problems. When an accounting firm has professional liability insurance, it means that the insurer will either:

  • Compensate the affected client, ensuring that they will no longer be able to sue the client accounting firm upon accepting the agreed compensation deal


  • Pay for legal expenses, should it become necessary for a client firm to defend themselves against an unavoidable lawsuit

Note that the best accountants professional liability insurance policies will cover almost all accounting mistakes related to misinterpretation, inaccuracy, and even negligence on the client firm’s part.

In addition to creating a provision, you should try to use AI technology to automate certain tasks that are prone to human error. This will help reduce the risk of costly mistakes. You can also use AI tools to review work for errors.


Choose Gradual Digitization

Digitize every aspect of your accounting firm that can be digitized but do so gradually. Not every accountant in your firm will be up to date with the latest software tools, so give them the time to get themselves acquainted with new tech. Overwhelming the workforce with rapid changes can and often does make the whole process of digitizing or updating an accounting firm’s business process a counterproductive approach. Instead, introduce tech at a consistent but gradual pace, supplemented with the necessary training to operate the software when needed.


ML is Key to Improving Efficiency in the Accounting Sector

The accounting industry is adapting in response to advances in AI technology. More accounting companies are using machine learning to address some of the most pressing challenges facing the industry.

Sean is a freelance writer and big data expert. He loves to write on big data, analytics and predictive analytics.