Most professionals have only a vague understanding of what Artificial Intelligence (AI) is or means. Absent an understanding of these capabilities, it is near impossible to have a working knowledge of the opportunities for applying AI to the accounting field.
What Is Artificial Intelligence?
According to BJ Copeland of the Encyclopedia Britannica, AI refers to the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans—the ability to:
- reason
- discover meaning
- generalize
- learn from past experience
Since the development of the digital computer in the 1940s, it has been demonstrated that computers can be programmed to carry out very complex tasks—as, for example, discovering proofs for mathematical theorems or playing chess—with great proficiency.
Still, despite continuing advances in computer processing speed and memory capacity, there are as yet no programs that can match human flexibility over wider domains or in tasks requiring much everyday knowledge. On the other hand, some programs have attained the performance levels of human experts and professionals in performing certain specific tasks, so that artificial intelligence in this limited sense is found in applications as diverse as medical diagnosis, computer search engines, and voice or handwriting recognition.
In addition, revolutionary, ambitious new projects, such as Elon Musk’s OpenAI project, are accelerating humanity’s course to achieving true Artificial Intelligence.
AI Suitable to Accounting?
In simple terms, technology won’t just collect information, it’ll learn from what it stores. As Xero notes, accounting software is getting smarter, automatically performing analysis which previously required human intervention. Consider tasks like bank reconciliation: systems can learn how to completely automate this job, freeing up your time.
The finance sector, given its heavy reliance on mass amounts of numbers and data, is a prime candidate for the automation offered by intelligent learning systems.
Accounting Applications of AI
Note the following examples of how AI can be implemented within your environment, as explored by Accounting Today’s report on the topic:
1. Clearing Payment Invoices
Today, accounts receivable or treasury clerks struggle to clear invoice payments when customers combine invoices in one payment, pay incorrect amounts or do not include invoice numbers with their payment. To clear the invoice, the employee either has to manually add up various invoices that might match the payment amount or contact the customer to clarify some information. In the case of a short payment, the employee either has to ask for approvals to accept the short payment or request the remaining amount from the customer. An intelligent system could help by immediately suggesting invoices that might match the paid amount and, based on established thresholds, automatically clear the short payments or automatically generate a delta invoice.
2. Expense Claim Auditing (AI enhanced automation)
Finance employees must ensure that receipts are genuine, match claimed amounts and are in line with company policy. What if AI and machine learning could support this process, audit 100 percent of all claims, and send only questionable claims to a manager for approval?
3. Determining bonus accruals
Deploying AI solutions, we could leave this calculation to a machine that uses all available system data and predictive analytics capabilities to come up with an unbiased accrual. Additionally, this would give accounting teams more time during the closing process for activities that require human intervention.
4. Automating Approval Workflows
Intelligent workflows could allow finance teams to distinguish and filter out the true exceptions from the standard low-risk exception that is usually approved anyway. This way, employees do not need to wait for approvals and feel empowered, while still limiting the risk for the organization.
Based on just these foregoing examples, it is clear that there is potentially a significant value-added from implementing AI, and it is an important trend in accounting to follow.