This probably isn’t the first article you’ve read on artificial intelligence this week. There’s a good reason for the ubiquity of AI as a subject: we’re experiencing an AI revolution right now. The technology is transforming how we live and work.
We hear a lot about how AI will profoundly change industries like accounting and finance, but less about how it’s going to achieve that — or what AI even does in these spaces. In this series of blog posts, we’re going to explore the specific functions of AI in accounts payable, starting with data analytics.
Some quick definitions
Before we explore the role of AI data analytics in accounts payable, we need to establish a few definitions:
Machine learning is a subset of artificial intelligence. It uses data and analytics to create programs that ‘learn’, using information to improve their performance over time in the manner of a human being. Home assistants use machine learning to recognise users’ accents and speech patterns. The word processing software I’m using to write this article has a machine learning function that suggests edits, and even preempts the words I’m about to type.
Big data is a term used to describe particularly large data sets — as you might have guessed. Specifically, data sets that are so big they can’t be processed or analysed manually, or even using traditional data processing software. Big data is a factor in many industries, and accounting and finance are no exception.
These two concepts underpin AI-powered data analytics, and it’s important to understand them before we go into more detail.
What is AI data analytics?
AI data analytics is the confluence of big data and machine learning. It uses machine learning algorithms to analyze vast quantities of data, recognizing patterns and anomalies — and extracting insights — with far greater speed and accuracy than any human can manage. In a data age when every industry is capturing enormous quantities of information from countless new sources, this facility is vital. Without the right tools in place to analyse it, we’d drown in a data deluge.
Data analytics can be divided into four separate disciplines:
- Descriptive
This is the simplest form of data analytics. It looks at the data to tell you the ‘what’ hidden in the information. For example, it might look at historical financial data to reveal a surge of invoice payments towards the close of Q4 each year. - Diagnostic
This form of data analytics addresses the ‘why’ of a particular trend in the data. If the company from our example above was a property management firm, they might find that their spike in invoices at the end of the year was related to increased maintenance requests caused by bad weather. - Predictive
Predictive analytics answers the question: ‘so what next?’ This is where AI really starts to add value. Our imaginary property management company might be expecting a similar spike in payments next year, but predictive analytics can crunch maintenance records, weather data, calendar idiosyncrasies, and countless other variables to reveal that Q4 of next year will see even greater demand than expected. - Prescriptive
Prescriptive analytics answers the most complex question of all: ‘what should we do about it?’ AI becomes particularly valuable here in suggesting future courses of action. Prescriptive analytics can help finance departments become more strategic, and plan cash flow throughout the year to prepare for this unusually expensive period to come.
How is data analytics used in accounts payable?
AP teams can benefit from AI data analytics in a number of ways, including the following:
Identifying operational efficiencies
AI can analyse data including payment windows and transaction times to identify bottlenecks and other inefficiencies in the AP process — helping teams reduce processing times and streamline their operations. AI data analytics can even help with financial planning and budgeting, by providing insights into cash flow projections and payment obligations.
“In financial reporting, AI technologies can analyse large amounts of data to identify patterns and trends that may not be visible to humans. This can help accounting professionals identify areas where financial performance can be improved, leading to more informed decision-making.”
- Paulo Jorge Ribeiro, CFO
Minimizing risk
AP data holds insights into potential risks and vulnerabilities, and AI-powered analytics can help uncover them. AI helps identify patterns or trends in the data that may hint at potentially fraudulent activities — such as duplicate invoices, unusual spending patterns, or unrecognised vendors.
AI data analytics can also help teams remain compliant — analysing the data to identify and flag instances of potential non-compliance for review, as well as suggesting courses of action.
Forecasting market trends
By identifying past trends and anomalies and extrapolating what might come next, data analytics provides an insight into what the market’s doing — or what it’s about to do. AP teams can use these insights to provide strategic support and decision-making to the wider business.
“The leveraging of big data by advanced analytics solutions can help develop deeper understanding of new market trends, identify new KPIs for performance management, and improve accuracy and timeliness of the forecasting process.”
- CPA Canada
Understanding and predicting behaviors
The insights AI data analytics provides can be used to better understand and predict the behavior of suppliers, vendors, customers, and competitors. Again, this information can help the finance department become more strategic as it moves further away from a purely back-office function, and proactively save costs throughout the AP process.
This article is the first in a series about the different functions of artificial intelligence in accounts payable. Keep an eye on our blog for the next in the series: AI fraud detection.
