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Finance Automation in '24: What It Is, Best Tools & 6 Processes

Cem Dilmegani
Updated on Mar 5
7 min read
Finance Automation in '24: What It Is, Best Tools & 6 ProcessesFinance Automation in '24: What It Is, Best Tools & 6 Processes

Finance automation is using the latest tech advances to automate finance operations. In traditional systems that support finance operations, such as traditional on-prem ERPs, financial procedures were slow and error prone due to manual work. But, thanks to the rise of machine learning and deep learning, automation rates can be significantly increased in finance operations. According to a Forrester report, 77% of finance organizations believe that business transformation relies on finance automation that delivers real-time data intelligence.1

These advances help companies increase their operating performance and allocate more time for higher value-added tasks while reducing costs by simplifying, accelerating, and automating processes in finance. Based on interviews with companies that rolled out leading edge finance automation solutions, businesses can save ~70% of finance operation costs, have faster turnaround times, fewer errors and less human intervention.

Which finance processes to automate?

1. Order to Cash (O2C)

Order to cash is the step-by-step process of receiving customers’ orders to receiving the outstanding balance.

2. Payroll Administration

Payroll administration can be defined as any of the activities required to arrange the compensation of employees for hours worked. These can include the control of overall hours served by staff, the rate of pay, and the allocation of compensation to staff.

According to McKinsey, the following tasks can be automated under payroll administration:

  • Flagging time-sheet errors and omissions
  • Auditing reported hours against schedule
  • Calculating deductions
  • Harmonizing data across multiple time-keeping systems

3. Source-to-Pay (S2P)

Source-to-pay (S2P) is the process of selecting a supplier for completing all their payments. It also includes the processes from requisition to payment named as purchase-to-pay or procure-to-pay (P2P). You can learn more about P2P from our articles on:

Since S2P processes include collecting invoice and payment data from multiple systems such as supplier emails, ERP, CRM, banks, retailers, and since not all of these systems have simple integration methods, they usually require some sort of manual labor. RPA bots may fill the integration void.

Albeit monolithic, P2P suites can offer inclusive solutions to companies in automating  workflows across an enterprise for requesting, procuring, receiving, and settling payments for products and services.

On the other hand, businesses can identify manual steps to automate and discover bottlenecks in these processes by using process mining tools.

Sub steps of S2P also have significant potential for automation:

Accounts Payable

Accounts payable (AP) processes include collecting, processing, and paying invoices from suppliers who have provided products or services to the client. In accounting, these transactions are written into accounts payable on the account before payment is made.

Before automation, finance teams used to go through invoices manually, and try to understand data, forward them to related systems to complete their records. They rarely spotted the anomalies in the invoice and contact the suppliers to solve the issues.

Now, invoice automation allows finance teams to concentrate on higher value-added activities. Invoice automation allows completely automated processing of most of a company’s invoices. This was in the past true for invoices received through Electronic Data Interchanges (EDI). 

Automation was also high for invoices with purchase order numbers (PO) since purchase order included all the necessary details about the invoice. Therefore getting the purchase order was enough to process the invoice.

To learn more from our in-depth analyses, check:

AI use in Accounts Payable automation:

The AP automaton and application of AI in accounts payable extends beyond just automating invoice handling. Machine learning models, once properly trained, can quickly interpret relevant data, significantly speed up the automation workflow. Utilizing continual learn and are specifically tailored to a company’s data boosts the speed and effectiveness of these processes. Some of the additional steps in automation include:

  • Data capture
  • Coding
  • Approver identification
  • Categorization of documents sent along invoices
  • Three way match
  • Sanctions screening

You can also check out our comprehensive, data-driven list and research articles on the topic:

4. Financial planning and analysis

Financial planning includes the unexciting task of the preparation and compilation of financial statements by a variety of departments in the Financial Planning and Analysis (FP&A) framework, which can be at least partially automated. The RPA tasks can include:

  • generating standardized financial reports
  • consolidating and validating budget and forecasts inputs
  • data collection and cleaning for analysis

5. Account reconciliation

Account reconciliation is another low-skill task for finance teams. However, any mistakes might cause significant disruption to businesses. With RPA tools, companies can:

  • log in automatically and extract relevant information from ERP systems
  • cross-check balances from the bank statement to general ledgers
  • prepare reconciliation statements on a standardized format

6. Financial close

Financial close is the monthly practice of going through a company’s transactions in the preceding month, closing out the temporary accounts, and posting the retained earnings onto the company’s permanent records.

Financial close is a lengthy process, comprising roughly of eight steps. During the process, accountants have to sort through all the transactions that have been carried out during the month, post them on the general ledger, reconcile the balances, post the closing entries, and create the company’s financial statements for the past month.

Completing these menial, labor-intensive, and time-consuming tasks manually could result in accountants overlooking some transactions, or making erroneous entries. That is why it could be useful for businesses to automate their financial close, thus allowing the software to take care of the process, under the oversight of the personnel.

We have an article that describes financial close in more detail.

Which technologies are used for finance automation?

This is not a MECE list as most of these technologies also rely on machine learning but it provides a list of the technology approaches in finance automation:

Document automation

Document automation enables the generation and processing of electronic documents.

Document generations systems involve logic-based systems that use pre-existing text and/or data segments to compile a new document. Generating standardized invoices or financial statements are some examples of this technology under finance automation.

More interestingly, companies use technologies like machine learning and optical character recognition (OCR) to auto-extract validate and enrich documents. This enables processing of most documents (e.g. invoices) in a completely automated way.

You can read our in-depth guide to learn more about document automation.

Robotic process automation (RPA)

RPA is a popular tool that uses screen-scraping and other technologies to create specialized agents that can automate secretarial tasks. In finance processes, RPA bots can run repetitive, rule-based monotone tasks and link disparate systems so that businesses can free humans from low-skill manual tasks and help them to focus on higher value-added activities. You can read more about RPA from our in-depth guide.

Process mining

Process mining allows companies to analyze their processes and identify the strengths/weaknesses of them to take action for improvement. With process mining tools, finance teams can discover whether their invoicing processes take too long or they cost more than they should and the teams can discover what steps they can automate in their source-to-pay processes. To learn more on process mining in finance, you can read our 7 use cases of process mining in finance.

Read our in-depth guide on process mining for more.

Chatbots & Conversational agents

A chatbot is a computer program that allows people to get information from machines using text and voice. In finance, conversational agents can be used as virtual assistants for finance teams or they can help automate communication between finance teams and suppliers or customers(Figure 2). Feel free to read our in-depth guide to learn more about chatbots.

Source: Zendesk 2

Machine learning

Rule-based automation helps businesses to define and execute requirements for different operations in compliance with the rules. Machine learning algorithms, on the other hand, learn from past transactions and customer decisions, perceive decision-making patterns and use these patterns to make future choices. As an example, finance teams can benefit from this technology to run accurate simulations and take precautions for possible adverse scenarios.

What are the main benefits of finance automation?

The benefits of finance automation include:

  • Reduced costs: Ardent Partners’ 2023 research shows that automated invoicing processes can cost between 40% and 90% compared to manual and paper-based processing methods.3
  • Faster processes: Businesses can handle more tasks with automation in the same amount of time. Ardent Partners’ report suggests that AP automation operated by best-in-class organizations can accelerate the average time to process an invoice by 81%. In case of accounts payable, this helps companies get discounts by making early payments. 4
  • Reduced manual errors: Automation lowers the errors occurred due to manual tasks. In surveys, reduction of manual errors is listed as top 2 benefit of RPA in general.
  • Improved visibility thanks to audit trail of automated operations
  • Employee satisfaction: Automation enables employees to focus on higher value-added tasks

What are the main challenges for finance automation?

Although the advantages of automating finance processes are numerous, businesses can still face particular challenges. They should first understand the root issues and then act to solve these challenges.

Businesses are hesitant to make big changes to their core processes

Since finance operations is the backbone of a business and is a small cost item for most businesses, businesses don’t want to make risky changes to finance processes.

Automation can result in low ROI

Some automation solutions require companies to invest hundreds of thousands to switch their systems. Such upfront investment would reduce ROI of automation initiatives. However, there are also vendors that offer more flexible payment schedules. One approach is to price products based on the benefits it generates. For example, some document automation vendors such as those in invoice automation, charge clients per document processed. This enables companies to test automation benefits without taking on significant financial risk.

In addition, companies tend to be focused only on labor savings while quantifying benefits of automation. Looking from a holistic perspective that includes more qualitative measures (like decreasing employee turnover, shifting talent to higher-value opportunities, minimizing potential rework) in addition to the typical quantitative cost measures will provide a more accurate assessment of the benefits of automation.

Automation that relies on process standardization can be delayed

It is easier to roll-out automation solutions in standardized processes. However, any large company has highly customized processes. Vendors need to be able to configure their solutions in line with the customized processes of the company to ensure a fast roll-out of the solution. Or they need to wait for companies to first standardize their processes while they roll-out the new solution.

If you are ready to use an account payable software in your business, we provide data-driven, sortable lists of vendors.

If you have questions about how to automate finance processes of your business, don’t hesitate to contact us:

Find the Right Vendors
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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