The role of the CFO is undergoing a profound transformation that Artificial Intelligence (AI) is helping to shape.
No longer confined to traditional accounting and compliance, CFOs are increasingly tasked with steering strategic decision-making and delivering actionable insights to fuel business growth. To help drive business performance, however, CFOs need an in-depth view of the organization which requires analyzing massive amounts of data quickly.
AI technology arrives at a crucial time in the CFO role evolution. Generative AI, in particular, has matured from experimental applications to solutions with tangible, real-world impact. Finance leaders can now tackle the complexities of data-driven enterprises by using AI to automate labor-intensive processes and deliver predictive insights.
In this article, we explore how CFOs in banking and insurance can harness AI to enhance decision-making, boost productivity, and redefine their role as strategic advisors.
The evolution of AI in real-world applications
To say that AI is the defining technology of the decade would be an understatement. Unlike previous trends that failed to meet their full potential, such as blockchain or CBDCs, AI distinguishes itself for one key reason: it’s a technology with real world value and existing use cases.
While AI technology has existed for years, it’s the newest applications that are seriously demonstrating meaningful, practical value.
The first technical demonstrations of AI date back to 1951, but despite being impressive, early AI lacked the sophistication required for broader use. This was primarily due to limited computational power, insufficient data availability, and a reliance on rules-based approaches that constrained the number of tasks an AI tool could accomplish.
Now, with generative AI (defined as a type of AI that can produce new content by learning patterns from existing data, as opposed to simply analyzing data) and the higher levels of computing power, the technology has reached a level of maturity where business and consumers alike can harness its full potential.
Note: For the purposes of this article, we’ll be using the phrase “AI” as an umbrella term for all possible variants from predictive to generative AI. For a deeper exploration of the various types of AI, refer to the ‘Evolution of AI’ section.
The impact of AI
The impact of AI is already being quantified. McKinsey Global Institute estimates that AI could generate between $200 billion and $340 billion in value annually, largely through gains in productivity. JP Morgan Research estimates it could increase global GDP by $7-10 trillion, or by as much as 10%.
As a result, leaders are eager to implement this technology into their organizations. According to a survey of 1,800 financial reporting executives done by KPMG, AI already accounts for 10% of the IT budget, and this figure is expected to rise significantly.
Generative AI is particularly relevant for industries like banking and insurance, which process vast amounts of data and often still rely on time-consuming manual tasks. AI can significantly reduce the time needed to close financial books, cutting it down from weeks to days. It also enables real-time updates for cash flow forecasts and frees finance professionals from repetitive tasks like reconciliation, regulatory reporting, and expense management. This shift allows professionals to focus on predictive forecasting, data analysis, and strategic business support, positioning finance as a catalyst for more informed decision-making.
This comes at the right time for many CFOs – AI could allow them to introduce new efficiencies and uncover relevant insights to support the business. CFOs are increasingly adopting advisory roles such as leading earnings calls and making critical business decisions. With the huge amount of data to process and ongoing responsibilities, such as closing books, ensuring compliance, and publishing financials, AI is becoming essential for CFOs to provide insight and direction to performance of the business.
AI and finance: A match made in heaven
Finance is fundamentally about the ability to understand large quantities of data as quickly as possible.
Decades ago, finance professionals could grasp the day-to-day operations at the bank and the broader economic landscape because the amount of information available was manageable. Prior to the widespread adoption of the internet, professionals received only a handful of data points.
In the 2020s, this is no longer true. As of 2021, a financial services employee has access to an average of 11 million files. The Bank of England has publicly stated that it receives around 35 million rows of data on derivatives and securities financing transactions every day. We’ve reached a stage where the scale of data far surpasses the capacity of human analysis—especially within the required timeframes. The traditional model of relying on monthly or quarterly reports to gauge business performance is increasingly ineffective; by month’s end, the data is already outdated.
This is why AI and finance are a match made in heaven: as banks transform into data-driven enterprises, CFOs rely on AI to manage the vast volumes of data flowing in daily. AI has become essential for maintaining oversight and extracting timely insights from billions of data points.
1. The evolution of the CFO role
Over the past few decades, CFOs have increasingly moved beyond traditional accounting roles and taken on leadership and decision-making roles within banks.
Nowadays, CFOs are the ones leading earnings calls, driving financial performance and giving the right guidance to key decision-makers. New concepts such as funds transfer pricing (forcing banks to accurately price their use of capital and liquidity pricing their product appropriately) are now falling on the CFOs lap, as are other functions such as implementing new technologies, integrating ESG requirements, adjusting the bank’s business model and reducing cybersecurity threats.
“If you go to bank earnings calls, who leads bank earnings calls? Who’s responsible for the share price of a bank? The CFO is, and that’s developed over the years. Now CFOs are responsible for driving the earnings of the bank.” – Joel Feazell, Head of Liquidity Management Solutions
To effectively address critical business questions and make informed decisions, CFOs must possess a profound understanding of their organizations. This depth of insight can only be achieved by assessing and synthesizing the billions of data points they encounter daily.
This is where AI comes in. AI can analyze vast amounts of data very efficiently in a way that elevates the work of employees, helping them get rid of repetitive, tiring and error prone work. For CFOs, this means faster information processing, enabling swifter and more accurate decision-making.
It is evident that AI is an essential tool for CFOs. The pressing question now is: How can it be effectively implemented? What strategies can CFOs employ to maximize the benefits of AI?
2. The evolution of AI: from process automation to Generative AI
AI has been around for decades, so what accounts for the current surge in interest around it? The answer lies in Generative AI (GenAI), a transformative approach that enhances creativity and contextual understanding, enabling machines to produce original content and insights in ways that previous AI technologies could not.
In the past, automation was mainly rules-driven and lacked true intelligence. It relied heavily on individuals to input “if this, then that” rules, requiring extensive feature engineering to ensure the tool would act based on specific criteria. This system was highly dependent on people to create, maintain and update those rules. The best real-life example of this is the credit risk scoring system which banks heavily invest in to automate a large part of the decisioning process. For example, if the applicant’s credit score is above 700, then approve the loan.
In contrast, Generative AI operates based on sequence-based modeling and probabilistic decision-making. It’s more contextual in its ability to consider other elements to define what the next step and outcome might be. This approach is considered “intelligent” because it moves beyond rigid rules and allows AI to exercise judgment instead. As a result, it adapts and improves based on the data it processes.
3. What are the different types of AI?
AI is a broad phrase used to describe different forms of non-human intelligence, but it includes various levels, all with distinct capabilities. Predictive AI has been in use for several years, and it’s Generative AI that is now driving advancements in technology.
In this article by BCG, the authors break down the different levels of AI and explain the role that GenAI and Predictive AI both play in a bank’s system, making a comparison to the left and right side of the brain.
“This left brain comprises algorithms that assign probabilities, categorize outcomes, and support decisions. For its part, GenAI acts as the right brain, wired to excel at creativity, expression, and a holistic perspective—the sorts of skills required to generate plausibly human-sounding responses in an automated chat.”
AI’s evolution from predictive to generative systems represents a fundamental shift in its role within industries like banking.
The main benefits for banks in a more evolved AI include:
- Faster and easier access to all data from a single interface
- Reduction in resources and technical expertise required to gather information
- Improved user comprehension of the data
- The capability for users to retrieve data using natural language queries
4. Why is Generative AI gaining prominence now?
Generative AI is gaining prominence due to the explosion of data from sources like social media and IoT, alongside advances in big data platforms and cloud computing that provide the processing power needed for real-time analysis and creation.
Tools like ChatGPT have also brought generative AI’s potential into the spotlight, showcasing its ability to generate human-like text, images, and even assist in decision-making.
The material difference [between GenAI and predictive AI] is that there’s the concept of intelligence, which improves over time and with more information and is a lot more flexible. Whereas a rules engine is dependent on the people that are creating the rules.” – Cameron Jarrett, Head of Sales, Canada
Embracing AI to redefine the CFO role
AI represents a critical tool for CFOs navigating the complexities of today’s data-driven financial landscape. By automating routine processes and enabling deeper insights, AI allows CFOs to shift their focus toward strategic priorities, such as guiding organizational performance and making data-informed decisions.
As AI adoption grows, the ability to use it effectively will be a defining characteristic of forward-looking financial leadership. By embracing AI as a foundational capability, CFOs can better position themselves to address the challenges of modern finance while delivering measurable value to their organizations.