Artificial intelligence in finance: here’s what to know.

Artificial intelligence (AI) in finance is the ability for machines to augment tasks performed by finance teams. For CFOs and finance professionals, AI represents the next major shift in financial technology.


What is artificial intelligence?

AI is the ability for machines to perform tasks traditionally seen as requiring human intelligence. AI analyses and learns from data, recognises patterns and makes predictions. By performing these tasks at greater speed and scale, AI can enhance intelligent decision-making and human productivity.


What is machine learning?

Machine learning (ML) is a subdiscipline of AI. ML models rely on data and self-modifying methods to identify patterns and make predictions or generate content. Those models can then continuously refine themselves to generate stronger future outcomes.



Learn about AI in finance.

For businesses to succeed in the new world of work, applications with AI at their core are now a necessity. Learn how AI can enable the finance team of the future.


What is cloud-based finance?

AI and ML are already transforming the finance function across organisations. But that transformation depends on the technology foundation of a financial management system.

Finance is defined as the management, creation and analysis of money and investments. Certain aspects of banking and finance are undertaken by dedicated financial institutions, such as credit scoring, underwriting decisions and fraud detection. Other areas are managed internally by organisations, such as risk assessment, budgeting and planning investments.

Many organisations will use financial management solutions to better inform their decisions. These solutions have long been the backbone for accounting and finance departments, and are typically part of a broader suite of applications known as enterprise resource planning, or ERP.

Historically, ERP systems have been held back by their legacy origins, with long, costly upgrade cycles; the need for IT to add or modify functionality; and frustrating data silos. Shifting to a native cloud approach such as Workday Enterprise Management Cloud gives organisations access to their data in real time, revealing a complete picture of your business and its finances.

Workday has embedded AI and ML into the very core of our platform. Leading finance organisations are already using AI and ML technologies in Workday to help deliver better employee experiences, improve operational efficiencies and provide insights for faster data-driven decision-making.

“Workday Journal Insights means one less thing for our end users to check off their list at the end of the month. They can correct issues and can fix them throughout the month. It makes it a continuous process.” 

– ERP Business Analyst, IMC Financial Markets


What is AI in finance?

The world of work has changed, transforming every aspect of the business. HR has shifted to a skills-based economy. Finance has embraced touchless transaction processing. And IT must manage tools and systems for a distributed workforce. This transformation is inseparable from AI and ML.

AI in finance is the ability for machines to perform tasks that augment how businesses analyse, manage and invest their capital. By automating repetitive manual tasks, detecting anomalies and providing real-time recommendations, AI represents a major source of business value.

Applying AI to predictable finance processes and tasks that are traditionally labour-intensive is essential for modernising the financial services industry. For example, finance teams have traditionally spent an inordinate amount of time gathering information and reconciling throughout the month and at period end. AI focuses on oversight such as addressing anomalies, managing exceptions and making recommendations so teams can focus their time on strategy.


How is AI used in finance?

The profile of artificial intelligence has risen massively recently, mostly as a result of ChatGPT, customer service chatbots and generative AI. Likewise, credit decisions that previously required people to process vast amounts of customer data and credit history are now accurately informed by AI systems.

For organisations, AI and machine learning algorithms have become necessary to remain competitive in finance. Traditionally, day-to-day finance functions – from detecting anomalies to identifying fraud to predicting outcomes – were done manually. Now, as finance faces increased expectations to work efficiently and provide strategic insight, organisations must adopt AI technologies that offer greater automation, integrity and accuracy.

In this section, we explore three areas where AI applications are fast becoming industry standard for the financial sector.

“We understand the opportunity that artificial intelligence offers to positively transform how people and organisations operate.”

– Sayan Chakraborty, Co-President, Workday

The role of AI in accounting.

Minimising human error is of utmost importance for accounting departments, as the consequences of incorrect numbers or inaccuracies are huge. Due to the high volume of invoices, reports and data that teams have to process, AI is increasingly required in order to stay competitive. These are the areas where AI is proving to be the most decisive for customers who use Workday:

  • Automation: By automating core financial transactions that have traditionally been performed manually, everyone benefits from reduced errors, costs and hours. For example, machine learning can upload and scan invoices in bulk, identify urgency and prioritise for processing. Then, invoices can be routed to the specialists best aligned to support based on past assignments.
  • Anomaly detection: Businesses that use AI to continuously surface exceptions and anomalies avoid bottlenecks at period end. The traditional record-to-report process involves a high volume of work in a condensed time frame. ML drastically reduces the time spent on oversight, enabling accounting teams to spend more time on analysis and tackle more strategic initiatives.
  • Intelligent recommendations: With AI, businesses can automatically generate recommendations continuously throughout the period. For example, in the contract-to-cash process 50% of receivables' time is spent processing manual payments. ML can detect inaccurate accounting, as well as recommend invoices that are the closest match to customer receipts.

“With the help of artificial intelligence and machine learning in our system, we’ve achieved nearly 100% billing accuracy and 100% automation of our cash flow, and the percentage of manual journal entries we now perform is incredibly low.”

– Philippa Lawrence, Vice President and Chief Accounting Officer, Workday

The role of AI in financial planning and analysis.

It’s never been more important to be able to forecast for the future. With major changes occurring on a weekly, if not daily, basis, businesses have to be more adaptive. AI algorithms can analyse data at the same pace as that rapid change, providing planning teams with the predictive power necessary to stay ahead of the curve. Here are three areas where AI is already critical for Workday Adaptive Planning customers:

  • Anomaly detection: AI uses historical data to alert planners when plan data falls outside of normal ranges. This significantly enhances a user’s insights into the root causes of data anomalies, allowing for timely data adjustments, if required. Each new case enables the model to learn from user feedback, constantly improving in the process.
  • Outlier reporting: AI can drastically reduce the response time in discovering outlier forecasts. In real time, AI can compare its own predictive forecast with a planner’s forecast, budget or other version. Then, it can further identify accounts with significant differences. In doing so, AI helps promote faster analysis across planning versions, while also uncovering anomalies as they occur.
  • Predictive forecasting: Accurate forecasts are at the heart of financial planning and analysis (FP&A). With ML, users can take advantage of historical data to further drive predictive demand forecasts. Thanks to AI’s real-time analysis, it’s also possible to incorporate other data sets to drive greater precision. This opens the door to a new kind of planning that continuously learns from data and adapts to a changing world.

The role of AI in procurement.

To operate effectively, procurement teams need to be empowered to accurately assess data, detect risks and drive efficiencies. Source-to-pay processes are often labour-intensive and error-prone, providing an opportunity for AI to deliver massive impact. Here are three examples of AI and ML use cases for spend management:

  • Locating data: Semantic search in Workday utilises optical character recognition (OCR) to quickly locate contracts. A task that previously took several hours or even days is now done in seconds. In doing so, you free up sourcing teams to spend more time on meaningful contract negotiations and risk management.
  • Detecting risks: Expense processing is a large area of risk for organisations. Rather than fraud, risks often stem from manual entry errors such as duplicate expenses, amount issues or incorrect expense items. This is where ML helps by reviewing large data sets and identifying items that seem out of the ordinary. In doing so, you streamline the expense report review process and speed up reimbursement times.
  • Recommending spend categories: ML can provide users with a choice of appropriate spend categories when creating a requisition or purchase order. In doing so, it reduces the number of downstream errors, speeding up the purchasing process and improving the user experience. Not only is this more efficient, it also promotes greater confidence among your team members.

Get ahead of the curve.

Despite the vast majority of finance professionals believing that AI and ML will be a part of their workflow by the end of the decade, only a small minority are already utilising the technology.


of finance professionals believe they will be utilising AI and machine learning by the end of the decade.*


claim to have it in place now, leaving 26% who say that it is either not achievable by 2030 or they don't need this capability at all.*

* “Future of Artificial Intelligence and Machine Learning in the Finance Function Global Survey”


What are the benefits of AI in finance?

CFOs have long been looking to reduce the time spent on processes such as close, consolidations, reporting and payroll. In the right hands, digital technologies and greater automation can be a fantastic combination for CFOs to transform the finance function.

A 2022 Workday report predicted that AI and ML in the finance function would experience substantial adoption (71%) by the end of the decade. Despite this, 74% of finance professionals currently have no experience whatsoever with AI. Here are several processes where implementing AI is already driving improved performance:

  • Analyse high-volume transactions faster
  • Automate manual and repetitive tasks
  • Predict and reduce risk effectively
  • Continuously detect patterns and anomalies
  • Reduce time to close the books
  • Free up employees to focus on other tasks
  • Limit chances for human error

A global Workday survey of 260 CFOs found that nearly half (48%) plan to invest in technology to streamline finance tasks. Even more significantly, nearly all (99%) of those making technology a priority agree that technology updates will be integral for both attracting and retaining employees. To stay ahead of the curve when it comes to hiring, businesses have to prioritise cutting-edge AI and ML solutions. 


Did you know?

Finding talent with corresponding AI and ML technology skill sets is a priority for 57% of CFOs when searching for new hires.


What is the future of AI in finance?

Any attempt to modernise finance without AI and ML is destined to fail. To unlock the true value of AI, organisations must have a strong understanding of its scope, from deep learning to natural language processing. Our research shows that many businesses are facing a major AI skills gap, with 71% of finance functions hoping to increase their data scientist headcount to meet their objectives by 2030.

As organisations continue to place a greater focus on AI, it’s critical that business leaders can trust their AI. At Workday, our approach leverages ethical AI principles that are built into the architecture of our finance solutions. 

Companies will often describe their products as “AI-powered” without a clear explanation of what that means. Workday is the only major cloud financial management provider that embeds AI and ML into its foundation. That enables our applications to natively leverage AI and ML as part of the workflow, rather than through complicated integrations.

We believe that for AI and ML to truly deliver on its future promise, it must be trustworthy. That’s why Workday has a Machine Learning Trust programme that provides governance in defining procedures for the development and management of AI and ML at Workday. This work helps us deliver technology benefits to our customers in line with our core values.​ As part of our commitment to trustworthy AI, we embrace the following principles:

  • Amplify human potential
  • Positively impact society
  • Champion transparency and fairness
  • Deliver on our commitment to data privacy and protection

See how Team Car Care drives results with intelligent planning.

Lead and shape the future of finance

With more than 60 million global users on the same version of Workday, only our customers have the trusted financial data necessary to realise the potential of AI. Learn more about how enterprise companies are currently harnessing native AI in their finance decisions with Workday. 

Ready to learn more about AI in finance?