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Top 5 Accounting Automation Technologies & Use Cases in '24

Top 5 Accounting Automation Technologies & Use Cases in '24Top 5 Accounting Automation Technologies & Use Cases in '24

There is increased interest in accounting automation today. The technology is used to automate most manual, time-consuming, and error-prone tasks in accounting. But the convenience and the ambiguity of the technology could lead to unfamiliarity with the exact nature of the tools it leverages. After all, all we hear is that solutions automate a task, but we don’t always get told how. An analogy is that we all know what a coffee maker does, but how many of us are familiar with the inner workings of the machine? 

In this article, we explain specifically the different technologies that are used in accounting automation, and their use cases. 

1. RPA 

RPA stands for robotic process automation. These software bots leverage screen scraping to record how a user interacts with GUI elements to complete a task, and replicate these steps to automate processes. Its use cases in accounting include:  

Financial close 

Financial close is the closing of a company’s temporary accounts at the end of each fiscal period. There are subprocesses, such as journal entry and reconciliation, that if done manually, can be time-consuming and error-prone. 

RPA bots can be programmed to be scheduled to automatically make journal entries by identifying transactions, approving them, and then making entries of the journal. Following that, with the help of RPA, the solution carries out reconciliation of all accounts. 

Accounts payable 

Accounts payable are the outstanding debts of the company to its vendors that need to be settled. A gap in the timely paying of invoices might result in reputation loss and supply chain disruptions. That is why it’s important for payments to be made as scheduled. 

RPA bots can be scheduled and commanded to set a chain of steps in motion every time an invoice is received. The sequence would look something as such: 

  1. Receive the invoice, 
  2. Read and capture the data  on the invoice by leveraging OCR, 
  3. Approve the invoice by sending it to the department working with the vendor, 
  4. Use EDI to send the invoice data to the accounts payable software to make an entry on the ledger, 
  5. And make payments on time by working in tandem with payment processing software. 

Payroll processing 

Payroll processing is the practice of paying the employees. But there are rules and regulations that should be factored in the process, such as a pension, insurance, and tax deductions. Moreover, companies have to make sure that they are paying all their employees, and the wired amount matches their contract stipulations. 

Payroll software is the general solution for processing payments to employees. But to take into account the factors of interest mentioned above, the software should be implemented in tandem with other ERP systems, such as benefits administration software that keeps track of employee benefits, corporate tax software that deducts employees’ taxes in line with the latest regulations, compensation management software that records the compensation packages and bonuses, and more. 

For the electronic data interchange (EDI) to happen between the solutions, and their coordinated functionality prior to wages’ calculation and processing, orchestration tools are installed atop. These bots are then programmed to automate the interaction between the ERP systems (take X from Y and send it to Z).  

2. OCR

Previously we mentioned that RPA bots can automate the extraction of data from an invoice. It’s important to know that RPA bots are like sports coaches managing the players. They do not do their job for them, instead of telling them what to do. 

The technology used in reading and extracting data from invoices received from a multitude of sales channels is called OCR, or optical character recognition. OCR is the technology that transforms human-written text into a version of the text that is machine-encoded. 

Invoice understanding 

Accurate invoicing is paramount in approving and entering transactions data on the books. A modern business today might receive invoices from many channels (online retailers, email, in person, etc.). Gathering all the information and inputting them accurately on receivable accounts is a time-consuming process. 

OCR in invoice generating software allows it to identify “shadows” on an invoice as the alphabets, read them, extract the data, and then neatly put it on the blanks on pre-made templates. 

Example of an automatically-generated invoice

Source: Klippa

3. NLP

Natural language processing is a field of artificial intelligence (AI) that is able to understand the human language in terms of meaning and tone. 

Internal & external communication 

Prioritizing which emails or messages to get back to is important to process continuity. It wouldn’t be productive if accountants prioritized, for instance, getting back to a vendor’s salesman over addressing an email from the bank for the company’s cheques not clearing.  

Gmail uses NLP to give “star,” and thus a priority, to emails sent from contacts over those sent en-masse by companies’ sales and marketing teams. The same technology is also used on internal messaging applications that companies use for internal communications. NLP is a useful technology that can read messages instantly and prioritize them based on their sentiment. 

4. ML

Machine learning is a subfield of artificial intelligence that relies on algorithms that can be trained to learn from incoming data to optimize the final results. 

Forecasting models 

Forecasting models are models that aim to predict the future based on past experiences with data. In accounting, they can be an extremely useful tool for forecasting the cash flow of a company, a variable that’d go on financial statements. 

Forecasting solutions in the market today can leverage ML to make predictions on the future cash influx into the company based on historical data. And the more the technology has had experience with data, the more accurate its estimates are. 

For instance, assume a company starts using a forecasting solution in January. For February, the solution compares the sales amount with that of January and creates a forecast. When March rolls around, the solution sees that it’d overestimated the forecast of February cash flow because it’d forgotten to seasonally adjust the data. So when the accountants run the model for March, this time the solution automatically seasonally adjusts the data. 

5. Workload automation 

Workload automation (WLA) is the general process of defining, scheduling, and executing tasks across different business platforms. 

Record-to-report (R2R) 

R2R is the overarching process of gathering data from multiple business sources, inputting them, analyzing them, turning them into actionable financial statements, and finally reporting them to the executives. But the process itself has many subprocesses, mainly tasks embedded in the financial close process. 

Workload automation can be leveraged to automatically record transactional data on the journal, approve them, move them onto the general ledger, reconcile the accounts, and create financial statements. The benefit of WLA is that each subprocess can be scheduled to happen at different intervals by schedule triggers. So an entry can be scheduled to happen every five minutes or when a triggering event happens (e.g. email from the vendor is received), while a report can be scheduled to be created at month’s end. 

For more on finance

If you are curious about other technologies used in the financial sector, read:

Finally, if you believe your business could use an accounting/fintech solution, we have data-driven lists of vendors prepared for different processes:

We will help you choose the best one for your needs:

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Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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