Tight (and sometimes unrealistic) timelines are a major challenge for FP&A professionals.
These deadlines often come from the C-suite, who don’t fully understand the time required to build accurate and reliable financial models.
This pressure gives FP&A teams less time to:
Consolidate data from different sources
Analyze trends and incorporate insights into forecasts
Validate assumptions and make accurate data-driven decisions
Explore more than one potential scenario, which compromises the quality of insights
As a result, forecasts can diverge significantly from reality, leading to considerable variances that need to be justified, only further increasing your team’s workload and stress levels.
To meet these tight deadlines, consider using AI as an assistant in your financial modeling and forecasting processes. This reduces the time your finance team needs to create accurate forecasts and build models, providing the rest of the business with real-time access to accurate, up-to-date data.
This guide breaks down the benefits of using AI for financial modeling and forecasting, and exactly how to use it to speed up your workflows and boost your FP&A team’s productivity.
The number one benefit of applying AI to financial modeling is its ability to automate routine tasks and free up time for you and your team to focus on more value-adding work. AI can analyze vast amounts of historical data in seconds to identify patterns and trends, provide accurate forecasts and reduce errors and variances that occur with manual data handling.
Rob Drover, VP Business Solutions at Marcum Technology, puts it this way in an episode of The CFO Show on the value of AI for FP&A teams:
When we think about why people are implementing AI-based solutions, it's about trying to free time up with automation to be able to do more value-added, strategic-thinking tasks. We probably spend about 90% of our time trudging through the numbers and 10% actually analyzing and coming up with new strategies and plans for the future. If we could achieve a 70/30 ratio or even an 80/20 ratio, it would make a tremendous impact on the quality of decisions that organizations make, improving their ability to adapt to new data and make better decisions. Small, incremental improvements like this frees up four to five hours of someone's week and positively impacts the quality of the work they do.
Before large language models became mainstream in 2023, financial analysts relied heavily on custom modeling with standard analytical tools like Excel to build different types of financial models.
While these tools provide flexibility, they require significant time and manual effort. When creating financial models in Excel to answer a simple question, multiple team members have the tedious task of gathering, entering and reviewing data from various source systems to identify and correct errors and standardize formats. And without real-time access to the underlying source data, financial models are realistically only updated monthly or quarterly, resulting in stakeholders making decisions based on outdated information.
But today, finance teams can use AI-driven FP&A tools to:
Automatically pull data from ERP systems and other sources in a centralized database and gain insights from it using natural language prompts
Provide real-time dashboards that automatically update financial metrics
Identify trends and predict future sales with higher accuracy (half of companies who use AI for forecasting see a 20% decrease in their overall forecast error)
Create dynamic financial models that update in real-time
Continuously monitor and assess risk factors
Another benefit of AI systems is that they can handle large volumes of data and complex computations quickly, making it easier to scale your financial modeling efforts without investing more time and money.
AI tools purpose-built for FP&A can also use machine learning algorithms to quickly analyze data and generate forecasts, enabling quicker response times to market changes and management requests, which is especially helpful when navigating challenging or volatile business environments.
A common use case of AI in FP&A is taking over routine, repetitive tasks that can otherwise take hours or days to complete. This is especially helpful in the case of data analysis and forecasting.
Howard Dresner, Founder and Chief Research Officer at Dresner Advisory Services, puts it this way:
When it comes to using AI for complex forecasting, you need a lot of external data to understand how to plan better because that's everything. If you don’t plan for demand appropriately, that can have some negative impacts on revenue and profitability. AI helps you bring in all the facets—both internal and external data—so you can create forecasts a lot closer to reality and come up with better plans. This way, you can execute knowing that you are as close to what the reality is going to be as you possibly can.
While processing large volumes of data from various sources, AI helps you spot patterns, trends and anomalies within financial data, which could indicate potential errors, deviations from plan, seasonality, or fraud.
With FP&A tools that combine large language models with your existing business data, it’s now possible to get quick answers to questions stakeholders have using just natural language. This means no one on your team has to manually dig through data just to find the right answer, in many cases eliminating the need to produce a full financial model altogether.
Instead, you or your team only have to type a simple, relevant prompt, and the generative AI can pull the data on your behalf and provide helpful responses in seconds.
The above example shows how you can use Vena Copilot (Vena’s new Complete Planning AI Assistant) to carry out what-if analysis for revenue planning. Vena Copilot can provide you with answers in just seconds, saving you the trouble of creating a full financial model from scratch. You can also download the source data used to produce to response, allowing you to investigate further.
Now, let’s say you wanted to get a picture of your company’s operational expenses (OPEX) broken down by department. Vena Copilot can automatically gather that data from all departments to give you this overview—eliminating the need for you to open multiple reports.
For stakeholders who frequently have questions for your FP&A team, you can grant them access to Vena Copilot (as long as they have a Vena license), allowing them to source their own answers to questions like how much remaining budget they have, saving considerable time for your team.
Other ways you can lean on AI to support your financial modeling and forecasting include:
Revenue Forecasting: predicting future revenue based on historical sales data, market trends and other relevant factors
Budgeting and Planning: tracking budget versus actuals to ensure alignment and make necessary adjustments
Expense Management: analyzing spending patterns and identifying areas to reduce cost, optimizing budget allocations and forecasting future expenses
Cash Flow Projections: analyzing cash inflows and outflows to account for seasonality, payment cycles, and other variables
Scenario Planning: simulating various business scenarios to evaluate the impact of different market conditions, policy changes, or business decisions
Risk Management: analyzing historical data and market indicators to identify and assess financial risks and proposing strategies to mitigate risks
Gartner predicts that 80% of large enterprise finance teams will rely on internally managed and owned generative AI platforms trained with proprietary business data by 2026.
While it may take a little longer for companies of different sizes to adopt the technology, consider implementing it to speed up your workflow.
Here are some steps to help you start:
First, identify challenges and inefficiencies in your current FP&A processes, then choose the tasks you want to automate with AI. This could include reducing forecast errors, improving data consolidation or enhancing real-time decision-making. Talk to other members of your finance team to understand where they’re experiencing the most pains.
Select AI-driven platforms that let you build and train your own AI models to meet your business’s specific needs and integrate well with your existing systems like your ERP, CRM and HRIS. Look for easy-to-use solutions that offer features like
User-friendly, familiar Excel interface (allowing you to dig into the AI-generated results in a familiar format)
Real-time data integration (to ensure your data is always up-to-date)
Pre-trained on common FP&A use cases like revenue forecasting, budgeting and planning, expense management and scenario planning
When you first start using the AI tool for financial forecasting and modeling, it's important to verify the output it produces. Initially, the tool may need time to learn and adapt to your organization’s specific needs and processes, much like a new human employee.
During this period, closely monitoring its performance and accuracy will help ensure the results are reliable and aligned with your business objectives. Providing feedback and making necessary adjustments will also help the AI tool improve over time. (With Vena Copilot, this is easy to do by adding new rules and rating responses generated in chat on whether the output was correct).
Begin with a pilot project to test your chosen AI tools and refine your approach. You might consider choosing a specific area of your financial modeling and forecasting process to apply AI, such as revenue forecasting or expense management. Measure your team’s efficiency and gather feedback from your team to identify areas for improvement. Once you have proven success, gradually scale up the implementation to other areas.
Financial modeling requires vast amounts of data, which includes sensitive financial information, and AI systems—like any software provider—are susceptible to cyber attacks aimed at stealing data, disrupting operations or manipulating outcomes.
There’s also the danger of having your information used to train the models, which is one of the risks of generative AI.
Using a closed large language model (LLM) is essential if you intend on using it for financial modeling and forecasting, for the following security and privacy reasons:
Your data is never used to inform public large language models and their outputs
Sensitive data does not leave the organization's secure environment, minimizing exposure to external threats
Access to the model is restricted, which limits the risk of unauthorized access to sensitive data
Data is processed locally within your organization’s secure infrastructure, reducing the risk of breaches during data transfer
Closed LLMs can be configured to comply with specific regulatory requirements (e.g., GDPR, CCPA), ensuring that data handling practices meet legal standards
Your organization has control over when and how the LLM is updated, ensuring that security patches and updates are applied on time
Vena maintains a number of procedures to comply with privacy (such as PIPEDA) and personal data protection (such as GDPR) obligations in the applicable jurisdictions. It also offers role-based access controls and compliance management. This allows finance teams to use the tool responsibly and in line with compliance requirements.
Implementing AI financial modeling and forecasting will bring more accurate predictions, improve efficiency and drive sales growth for businesses of all sizes. AI can analyze historical sales data and market trends to make more accurate forecasts, helping you better allocate resources and create effective sales plans.
For smaller businesses, AI will lower operational costs and free up resources without the need for additional headcount, allowing finance teams to focus on innovation and growth. AI insights can help them better understand market trends and customer behavior, giving them a competitive advantage.
Medium-sized companies will also benefit from AI's ability to analyze complex data sets, boosting efficiency and reducing errors. AI will enable more precise long-term forecasting and scenario modeling, helping these companies strategize better and manage risks effectively. This will support faster innovation and better decision making, facilitating their path to scale.
Enterprise businesses, for their part, will experience significant changes with AI through real-time financial reporting and predictive analytics, allowing for agile decision making and improved risk management. Automation opportunities from AI will result in cost savings, better regulatory compliance and easier global financial management, supporting multinational operations.
Just as AI has its benefits, it has some limitations for financial modeling and forecasting. We compare both in the table below:
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Pros and Cons of Using AI for Financial Modeling and Forecasting |
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Pros |
Cons |
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AI can analyze vast amounts of historical data to identify patterns and trends, leading to more accurate forecasts |
AI models require high-quality, clean data, so neglecting to ensure data quality in the first place can lead to inaccurate predictions and insights |
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Automating routine tasks frees up time for your team to focus on strategic analysis and decision making |
Handling sensitive financial data with AI systems requires robust security measures to prevent breaches and ensure compliance |
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AI helps identify and assess financial risks, enabling you to develop proactive mitigation strategies |
You may encounter resistance from staff who are accustomed to traditional methods and skeptical of new technologies |
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AI tools can handle large volumes of data and complex computations, allowing you to scale your financial modeling efforts without significant additional resources |
Dependence on AI for decision making can reduce human oversight and critical thinking |
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AI systems can continuously learn from your inputs, improving their accuracy and effectiveness over time |
Some AI systems can be complex to implement and require specialized skills and training |
Using AI as part of an existing FP&A software addresses many of the cons listed above.
For example, FP&A software directly integrates with your source systems, eliminating data integrity issues that can arise from copying, pasting and manually manipulating spreadsheets, as well as outdated data pulls.
These tools (like in the case of Vena) can also automatically identify deviations from the plans and rules you have set and keep track of where each new number input came from through audit trails. But, the quality of input data still relies on the original data sources, so it’s important to verify source data to maintain its integrity.
With the growing demands on FP&A teams—more departments requiring your strategic input and management requesting quick turnarounds—finding a reliable way to increase your team’s capacity is certainly top of mind. Generative AI can help you do just that.
AI-driven tools like Vena Copilot can take on many low-value tasks, freeing up time for you to play a more strategic role across the business and drive higher profitability.
The AI assistant works as an extension of your FP&A team, using natural language to gather data, generate reports, analyze trends, optimize forecasts and answer complex business questions. You can train it to meet your unique business needs, and it becomes smarter over time as it learns more about your business.
Anton Medvedev, Product Manager at Vena, puts it this way: “We want to help finance teams build a ‘copilot’ that’s specific to them. It’s all about enabling our business stakeholders to get their own insights.”
Plus, you don’t even have to be an expert in your company’s financials to use Vena Copilot; even non-technical users can use the tool to explore scenarios, create accurate forecasts and provide stakeholders with reliable insights they need to make quick decisions.
Ready to start creating and training your own AI models for your business’s unique needs? Learn more about Vena Copilot.
AI Financial Modeling uses artificial intelligence and machine learning to quickly analyze large amounts of historical and current financial data, find patterns and accurately predict a company's future financial performance. It automates routine tasks, speeding up the process and potentially creating more accurate forecasts and reports than traditional financial modeling techniques.
AI Financial Forecasting involves using artificial intelligence to predict future financial results by analyzing past data based on specific drivers set by your finance team. These drivers include internal factors such as sales volumes and product pricing, as well as external factors like customer demand and interest rates. These AI models learn and improve over time, providing more accurate and reliable forecasts than traditional methods.
To effectively use AI in financial modeling and forecasting, finance professionals need to understand their industry well, know how to analyze financial statements and be skilled in traditional financial modeling techniques. They also need strong critical thinking skills. These skills help them choose and interpret data correctly, validate AI models and make strategic decisions based on the insights provided.