AI In Finance: What CFOs Are Saying

For many businesses, the finance department is at the top of their concerns regarding compliance, accuracy, and timeliness. Now, imagine a world where your confidence in the financial components of your business is such that you can devote the resources you spend now to improving other business processes.

Financial processes are what distinguish good and great CFOs. See what we mean here.

While it might sound too good to be true right now, AI in finance is inching us closer to this reality each day.

AI in Finance

In fact, artificial intelligence is proving its worth in several industries, and finance is one of the most promising.

Employing artificial intelligence in the finance sector is not a new practice—financial institutions have been using it since the early 1980s. What has changed recently, though, are incredible technological improvements and access to greater amounts of data. This has facilitated AI’s ability to take on even more sophisticated roles within the finance department.

Artificial intelligence can process data at a rate that far surpasses any human capability. With the right algorithms, machine learning, and software, it can create incredible financial forecasting, detect fraud, and perform a number of other critical financial management tasks.

As with any major industry disruptor, the adoption of AI in finance hasn’t been without resistance. Understandably, there are concerns about what it means for human workers, privacy, and security. Deloitte’s survey expressed many of these concerns, but the survey also showed great interest and impressive adoption rates of AI.

Applications of AI in Finance

We’ll explore more of the survey’s specific findings in a moment. First, let’s cover how AI in finance is already being used.

Automated Financial Management

A primary use of AI in finance is to automate tasks for financial management. 

This includes tasks like:

• Processing invoices and receipts
• Reconciling accounts
• Managing budgets

These tasks once required manual labor to complete, but now, those same staff members can spend their time on other tasks that AI cannot yet handle.

Financial Forecasting

Financial forecasting forms the heart of many financial departments. 

To make reasonably correct predictions, it needs certain information, including:

• Current market information
• Historical data
• A comprehensive understanding of the business and the industry in which it operates

Financial forecasting benefits from AI because it can process vast amounts of data quickly and accurately. Artificial intelligence can create more dependable projections by scrutinizing past trends and prevailing market conditions than humans can. Or, at least, generate predictions that are just as accurate but in far less time.

Here’s another article that CFOs won’t want to miss next about risks and where AI falls into the picture.

Fraud Detection

Fraud proves to be a big challenge in the finance industry, whether from internal or external sources. In turn, most organizations do their best to be proactive about fraud and implement systems to detect it before the damage is done.

There are different strategies for doing this, but this is yet another area where artificial intelligence can make a profound difference. With the help of machine learning and complex algorithms, AI can spot patterns, inconsistencies, and other red flags that may signal fraud.  

Deloitte’s CFO Survey Findings on AI in Finance

Now, let’s take a look at some of the noteworthy takeaways from Deloitte’s survey regarding AI in finance. Although the survey covered a number of topics, we’re focusing on GenAI for the purposes of this blog.

Experimentation and Strategy

To begin, North American CFOs who were surveyed reported the following in terms of their current stance on using AI in finance:

• 42% of CFOs report that their organizations are currently experimenting with AI.
• 15% have already started integrating GenAI into financial management
• Approximately 17% of CFOs aren’t prepared to decide one way or the other about what they will do with AI in their organization.

Budgetary Considerations

The survey’s results indicate that most CFOs are taking a measured approach when allocating funds to AI in their budget.

Nearly two-thirds (63%) of CFOs expect to dedicate less than 1% of their budget to GenAI in the upcoming year. This caution reflects a strategy that does involve some experimenting, but also one that takes time to incorporate the technology without taking on significant financial risk.

Other CFOs are more invested in exploring the power of AI in finance: about one-third (33%) of CFOs plan to allocate between 1% and 5% of their budget to AI initiatives. This represents a more committed investment toward adopting AI once and for all.

Barriers and Concerns for Adopting AI in Finance

If a significant portion of CFOs are still unsure of how and when they will implement AI, this is indicative of a level of hesitation that many AI companies are actively trying to mitigate. AI finance platforms need to understand exactly where these concerns come from.

The 2023 North American CFO Signals Survey is a great place to start this research; it lists a number of barriers to adoption and some of the concerns CFOs have about taking this step.

• Impact on Risk and Internal Controls

57% of CFOs are concerned about the impact of GenAI on risk management and internal control mechanisms. This concern stems from the need to be sure AI-driven processes don’t solve one problem but introduce new ones, including failure to comply with existing regulatory frameworks.

• Data Infrastructure and Technology Needs

Slightly over half (52%) of CFOs worry about the data infrastructure and technology an organization will require for effective AI implementation. The process necessitates implementing robust data systems and advanced technologies, and to many, this seems like a massive undertaking.

Relating to this, 49% see acquiring the necessary resources as a major challenge.

• Financial Commitment

The financial commitment that can come with AI systems is also on the minds of CFOs—51% report concerns about how much they will need to invest in artificial intelligence technology.

CFOs also listed these other concerns:

• Governance (49%)
• Ethics (31%)
• Legal implications (30%)

These factors underscore the weight of the obligations of those responsible for integrating AI into financial management. These obligations include everything from the ethics of AI to its legal implications. 

• Personnel

Likely due to high demand levels thanks to fast adoption rates in many industries, finding data scientists and other AI experts can take time. A total of 63% say that a lack or shortage of required skills for the integration and use of AI is a significant hindrance among employees within various departments.

• Risk, Compliance, and Governance

On the topic of risk, whether perceived or real, 45% of CFOs are somewhat apprehensive about the risks and governance issues associated with artificial intelligence. Fortunately, as they learn more about AI in finance, CFOs will see that effective risk management and governance frameworks mitigate most of the risks posed by AI.

Survey results indicate an eagerness to understand artificial intelligence and its implications, including ROI, financial impact, and how they might apply the technology:

• 39% want to see more use cases that demonstrate the ROI of financial AI.
• 28% seek clearer evaluations to understand the financial impact of adopting GenAI.
• 26% are interested in becoming more familiar with the dangers and restrictions of AI
• 16% are eager for more insights into the potential and applications of GenAI

Other hurdles include competing priorities at 42%, privacy and security issues at 37%, and the potentially high cost of investment (30%). These challenges are part of wider strategic and operational concerns that CFOs face when they implement AI technologies into their businesses.

Speaking of ROI, learn how automation helps finance teams add value next.

Reasons to Use Artificial Intelligence in Finance

One of the most anticipated benefits of Generative AI (GenAI) in finance is cost reduction. More than half of survey respondents (52%) are optimistic about AI’s cost-saving capabilities.

There’s good reason for this optimism. Saving money on labor and resources is one big advantage of automating financial tasks. Not to mention, companies also benefit by fewer errors when data isn’t solely at the mercy of humans. 

Another advantage is how AI can improve customer experiences. Half of CFOs anticipate it will improve customer interactions and service delivery.

Increased margins, efficiencies, and productivity are among some of the other most appealing benefits. CFOs seem acutely aware of these benefits already: 45% anticipate that GenAI will boost operational efficiency along with productivity levels.

CFOs also seem interested in the future of AI and what it can mean for their financial processes: 38% see the development of new capabilities as a primary advantage of GenAI.

Improved forecasting and modeling is another benefit, cited by 33% of CFOs. GenAI can analyze vast amounts of data to provide more accurate predictions and insights. Finally, 30% of CFOs expect GenAI to provide more extensive insights that aid in decision-making.

Explore AI in Finance with Xledger

Now it’s your turn to learn more about artificial intelligence in finance. Whether you’re just dipping your toe in the waters of AI or you’re ready to start implementing rather than experimenting, Xledger is the way to go. Our cloud-based finance and accounting software leverages GenAI for impressively simple, automated, and smart financial management.

Book a demo today to see the application of artificial intelligence in finance in real time.

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