Machine Learning in Accounting: Revolutionizing Financial Analysis

Machine learning (ML), a branch of artificial intelligence (AI), is everywhere. It has transformed online shopping (“users also bought…”) and TV streaming (“because you watched…”). Machine learning has also revolutionized the field of accounting.

Machine learning in accounting has made financial management more streamlined, accurate, and insightful. Business owners, accountants, and bookkeepers can benefit from machine learning for accounting. 

What is machine learning (ML)?

Machine learning is the process of using data and algorithms to teach a machine (e.g., software). The machine learns from historical data to predict new data. 

How? 

The machine looks for patterns, relationships, and trends. Someone watched ABC, so they might like XYZ. 

Machine learning requires a deep dataset, time, constant evaluation, and monitoring to improve its accuracy. Even then, it’s not going to be 100% right (it’s a machine, after all!). 

How is machine learning used in accounting?

Machine learning for accounting can improve efficiency and accuracy, saving you precious time and headaches. 

Examples of machine learning in accounting include:

1. Task automation
2. Data entry 
3. Reconciliation
4. Fraud detection and prevention
5. Data-driven insights
6. Financial reporting
7. Forecasting and planning 

1. Task automation 

You have to do a million tasks to run your business. Invoice customers, update your books, pay vendors, run payroll, etc. The list goes on and on.

What if you could automate these tasks? With machine learning in accounting, you can. Software that utilizes machine learning can automate invoice processing, expense categorization, and more. 

2. Data entry 

Data entry is one of the foundations of accounting. You must record all financial information (e.g., transactions, invoices, etc.) in your books. Complete data entry ensures your records are accurate and organized for everything from filing your business taxes to making decisions.

Traditional data entry is monotonous, time-consuming, and susceptible to errors. Machine learning automates this tedious task. 

Machine learning pulls information from various sources (e.g., your bank account) and feeds it directly into an accounting system. 

3. Reconciliation

Bank statement reconciliation is the process of comparing your bank statements and accounting books to look for discrepancies. 

Machine learning can automate the reconciliation process to quickly identify discrepancies and reconcile differences. 

4. Fraud detection and prevention

Fraud is a big problem for small businesses. One report found that small businesses (with fewer than 100 employees) that suffered 2,690 instances of fraud had a median loss of $200,000.

Systems powered by machine learning can help detect and prevent fraud. Machine learning’s ability to analyze large amounts of transaction data allows it to identify patterns. It can then detect anomalies that indicate fraudulent activity, giving businesses time to take action before losing thousands of dollars. 

Machine learning can also flag potentially high-risk transactions based on historical data and trends. 

5. Data-driven insights 

Data entry is an important part of accounting. But the work doesn’t end after you enter the data. You also have to analyze it to understand your company’s financial health for informed decision-making. 

Machine learning in accounting can automatically analyze data to give insights into:

• Financial performance
• Customer behavior
• Market trends

6. Financial reporting 

Your financial reports are the key to your company. Reports include your profit and loss (P&L) statement, balance sheet, and cash flow statement. These reports must be accurate and error-free.

Machine learning’s ability to validate data can ensure your reports are accurate and insightful. Not to mention, the technology streamlines the process of generating reports, saving you time to get back to your business. 

Get the accounting reports you need with Patriot’s accounting software.

Generate your profit and loss statement, general ledger, and more in just a few clicks.

7. Forecasting and planning 

Predicting your company’s future can be challenging. You can’t see into the future, after all. However, you can use historical data to predict future expenses, income, and demand. 

Machine learning can analyze historical financial data to forecast future trends and performance. You can use ML forecasts to help with:

Example of machine learning in accounting

You know how machine learning can be used in accounting. Now, let’s take a look at machine learning in action.

Patriot’s accounting software lets new users import bank transaction data from up to two years ago. Two years of data could include hundreds of transactions. So, Patriot uses machine learning in its “Smart Suggestion” feature. 

“Smart Suggestion” provides automatic expense account suggestions to users importing bank transaction data. These suggestions save you from manually categorizing bank imports. 

Accept or dismiss the suggestions, and you’re done!

Benefits of machine learning accounting

Machine learning saves time, reduces the chance of errors, and improves the accuracy of financial records. 

ML can also reduce manual labor (and expenses) by automating routine tasks such as data entry, reconciliation, and report generation.

Take a look at a few benefits of machine learning: 

• Automate data entry
• Streamline monotonous tasks
• Detect fraudulent activities 
• Use historical data to predict future data 
• Audit your business records 
• Reduce operational costs through automation

Business owners and accountants can use machine learning in accounting to streamline processes and get back to value-added activities.  

Look for accounting software that uses machine learning technology to take advantage of these benefits. 

Challenges of machine learning in accounting 

Although there are several benefits of machine learning, ML is not without its challenges. Technology can miss something (e.g., a mistake or correlation) that a human would catch. 

ML learns through data. Poor data or small quantities of data can teach machine learning to draw the wrong patterns, trends, and relationships. 

What accounting problems can be solved by machine learning?

Machine learning can solve accounting problems like data entry, bank reconciliation, and invoice processing. 

Businesses and accountants can use machine learning to streamline tasks, catch errors, and reduce operational costs.

Will machine learning replace accountants?

The Big Four accounting firms (Deloitte, EY, KPMG, and PwC) stated that AI cannot replace human accountants. Accountants can instead use AI to boost their productivity and efficiency.  

According to PwC’s chief products and technology officer, Joe Atkinson:

“AI tools are really good at pulling out information and making predictive choices but they can’t replace human judgment.”

The field of accounting is certainly evolving with machine learning and AI. But at the end of the day, machine learning is a tool people can use—it isn’t a replacement for human decision-making and emotional intelligence.

This is not intended as legal advice; for more information, please click here.