FP&A teams have a lot to handle: endless spreadsheets, tight deadlines, and the constant juggling act of answering every question the business asks. Luckily, AI software for finance is becoming more accessible to finance teams everywhere and helping solve these challenges.
What Is AI in Finance?
AI in finance refers to the application of machine learning and advanced algorithms in financial processes to automate tasks, enhance forecasting, and support data-driven decisions. According to Markets and Markets, the global AI in finance market was valued at USD 38.36 billion in 2024 and is projected to reach USD 190.33 billion by 2030, representing a compound annual growth rate of 30.6%. The generative artificial intelligence market in banking and finance is projected to grow from $1.29 billion in 2024 to $21.57 billion by 2034, representing a CAGR of 31.64%.
Leveraging technology in FP&A isn’t a new concept. It started with the 1st generation of on-premise solutions, moved to the cloud with sophisticated 2nd generation planning tools, before bursting out of the spreadsheet with 3rd generation FP&A platforms.
How does the 4th generation of AI in finance look like?
AI streamlines reporting, improves forecasting, and reduces operational overhead by automating repetitive tasks and delivering real-time insights. Finance teams can now optimize processes, minimize errors, and focus on strategic initiatives that drive growth. Organizations implementing automated accounts payable systems report reduction in invoice processing costs by up to 81% and cut approval times by 73%.
Benefits of AI in Finance
AI streamlines reporting, improves forecasting, and reduces operational overhead by automating repetitive tasks and delivering real-time insights. Finance teams can now optimize processes, minimize errors, and focus on strategic initiatives that drive growth.
Use Cases of AI in Finance
AI software is already widely used to detect fraudulent transactions in real time, allowing organizations to minimize risk and protect customer data.
Credit scoring algorithms powered by AI provide quick, objective assessments, streamlining lending processes while reducing biases.
When implementing AI in finance, data privacy and regulatory compliance are top considerations. Organizations should ensure they handle finance data governance properly, storing sensitive financial information securely while adhering to relevant legal requirements. Approximately 70% of total trading volume in the U.S. stock market was executed through AI algorithmic trading in 2021.
Challenges & Best Practices
Regulatory Compliance and Data Privacy
When you can do more basic tasks faster, you increase productivity without putting additional pressure on an already demanding schedule. Crucially, using AI in this way frees up your time to focus on more strategic, value-added activities that drive your business forward. Notably, JPMorgan Chase increased its target for AI-generated business value from $1 billion to $1.5 billion in a single year, with more than 300 AI use cases in production.
Algorithmic Bias and Fairness
When you can do more basic tasks faster, you increase productivity without putting additional pressure on an already demanding schedule. AI has the potential to save finance professionals up to 12 hours per week within the next five years, equivalent to adding an additional colleague for every 10 team members. Crucially, using AI in this way frees up your time to focus on more strategic, value-added activities that drive your business forward.
1. Automate Mundane Tasks
Finance teams spend a significant amount of time on repetitive tasks like data entry, report generation, and basic analysis. But the latest generation of FP&A tools is starting to embrace AI software that automates these mundane tasks. Now, in just a few clicks, you can:
Pull operational data from your CRM, HRIS, or BI tool, then clean, sort, and map the data instantly.
Generate detailed reports in just a few clicks.
When you can do more basic tasks faster, you increase productivity without putting additional pressure on an already demanding schedule. Crucially, using AI in this way frees up your time to focus on more strategic, value-added activities that drive your business forward.
Did you know that… two-thirds (66%) of finance professionals believe AI will save between 50 and 200 hours of work annually, enabling them to focus on more strategic activities.
2. Generate Strategic Insights
It’s one thing to sort, model, and clean up data. But analyzing and presenting that data in a way that’s useful to the rest of the business is another matter entirely. JP Morgan Chase generated $1.5 billion in annual business value through comprehensive AI implementation across their operations.
The good news is that AI is bridging the gap between raw data and strategic insights quickly. A modern FP&A platform like Abacum uses AI to:
Analyze raw business data
Find clear trends and correlations
Write a detailed analysis for you
With AI taking care of the heavy lifting, you can identify new business opportunities, optimize resource allocation, and improve overall financial performance. This puts you in a stronger position to embrace finance business partnering, armed with the deep insights into business operations you need to contribute to the organization’s strategic goals.
3. Real-Time Risk Mitigation
Until recently, most teams had to wait until month-end to spot red flags and respond, which could significantly increase the cost of those risks. With AI, you get real-time analytics at your fingertips, providing up-to-the-minute insights.
FP&A solutions are now building the capacity to generate new forecasts with simple data inputs. Approximately 70% of trading volumes in 2023 were executed using AI algorithms, a figure expected to increase further. Abacum’s AI auto-forecasting handles complex scenarios and large datasets faster—and more accurately—so you can meet this rising demand.
No more waiting for end-of-month reports to make informed decisions. Automated financial reporting ensures finance teams stay proactive and responsive. Now, you can spot trends, identify issues, and make decisions much earlier, keeping your organization agile and responsive.
4. Enhanced Forecasting
Forecasting is the bread and butter of FP&A, but traditional methods can be time-consuming and often lack precision. There’s only so much time in the week, yet demands for additional forecasts from stakeholders grow every quarter.
FP&A solutions are now building the capacity to generate new forecasts based on simple data inputs. Abacum’s AI auto-forecasting handles complex scenarios and large datasets faster—and more accurately—so you can meet this rising demand.
Crucially, auto-forecasting uses machine learning models to optimize your forecasts based on historical data. This allows you to create more accurate forecasts, free of common biases that lead to over-committing or under-promising to align with overall business goals.
Embracing AI in Finance Right Now
AI is no longer just a buzzword. It’s a powerful tool that, when used in the right way, can transform the way FP&A teams operate day to day. Unlock the power of AI software to automate routine tasks, enhance forecast accuracy, provide real-time analytics, and deliver strategic insights.
There's no denying a popular narrative of “will AI take my job?” But AI isn't your competition. In reality, knowing how to leverage AI to be more productive and effective in your role will become your competitive advantage.
Whether you’re a seasoned finance pro or an FP&A newcomer, AI is set to be your secret weapon. It’s time to stop wondering about the future of AI and start leveraging tangible, practical uses of AI right now in FP&A. Find out how Abacum Intelligence takes your FP&A team to the next level, with the 4th generation of FP&A.
Future of AI in Finance
Looking ahead, AI is poised to transform traditional finance processes, from predictive analytics to automated decision-making. With continuous advancements in machine learning and natural language processing, finance teams will find more opportunities to streamline workflows, reduce risk, and inform strategic direction.
