Financial forecasting helps with accurate predictions so that businesses can make beneficial plans. It’s not a one-size-fits-all strategy. It varies depending on your data and the output you expect. Financial forecasting plays a significant role in evaluating performance and comparing budget vs. actual spending.
In this blog post, we briefly introduce what financial forecasting is, talk about some key use cases, and let you know the benefits. Let's give it a read.
What is financial forecasting?
Financial forecasting involves analyzing past and present events to predict future happenings. They are used by modern businesses to plan and adapt to uncertainties. With it, teams can predict future revenues, expenses, and cash flow. It helps executives gauge whether the company is heading right and make profitable financial decisions.
Accurate financial forecasting requires collaboration between experts, strategic business partners, and forecasting tools. It helps create a business roadmap for achieving growth and stability.
Here are some specific examples of how financial forecasting is used in real-day—
- A startup might use a financial forecast to estimate future revenue and expenses to secure funding.
- A large corporation might use a financial forecast to decide whether or not to invest in a new product or expansion.
- A government agency might use a financial forecast to predict economic growth and make budget decisions.
Limitations of traditional forecasting methods
Traditional forecasting methods, typically statistical or rule-based, face several limitations, including—
- Limited Data Handling: Most rely on historical data and assume linear trends, unable to effectively capture intricate patterns, hidden relationships, and non-linear dynamics within data. This leads to inaccurate predictions when unforeseen events disrupt established patterns.
- Black Box Nature: Many traditional methods lack transparency, making it difficult to understand the reasoning behind their predictions. This hinders trust and limits their practical application in scenarios requiring explainability and interpretability.
- Limited Feature Engineering: Traditional methods often rely on manually handcrafted features, restricting their ability to identify complex relationships and hidden insights within data. This can lead to overlooking crucial factors impacting the forecast.
Transformation in financial forecasting
The rise of big data and machine learning has significantly transformed financial forecasting for FP&A teams. Here's how:
- Advanced algorithms capture complex patterns and hidden relationships in data, leading to more accurate and granular forecasts. This allows for deeper insights and better-informed decision-making.
- Machine learning models can go beyond simply predicting future values. They can identify leading indicators, potential disruptions, and hidden trends, empowering proactive strategies and risk mitigation.
- With continuous data feeds and automated model updates, forecasts can be dynamically adjusted, reflecting unforeseen events and market changes. This ensures agile decision-making and adaptability to fluctuating environments.
Below is a look at past and current use cases of financial forecasting—
Past use cases
Historically, FP&A teams relied heavily on financial forecasting for several key tasks:
- Budgeting and planning
- Scenario planning and risk management
- Performance measurement and reporting
- Financial modeling and valuation
- Communication and investor relations
Current use cases
In light of advancements, FP&A teams are utilizing financial forecasting in even more creative and impactful ways like:
- Optimizing pricing and resource allocation
- Targeted marketing and sales initiatives
- Financial risk management and stress testing
- Continuous improvement and optimization
Types of financial forecasting methods
There are many different types of financial forecasting methods, each with its strengths and weaknesses.
Here are some of the most common types—
#1 Quantitative methods
Time Series Models
These models analyze historical data patterns to predict future values. Common examples include moving average, exponential smoothing, and ARIMA.
These models analyze the relationships between one dependent variable (e.g., sales) and one or more independent variables (e.g., marketing spending, and economic indicators) to predict future values. Examples include simple linear regression and multiple linear regression.
These models consider the relationships between multiple economic variables to predict future financial outcomes. They are often used for long-term forecasts.
Machine Learning Models
These models utilize algorithms to learn from vast amounts of data and make predictions. They are becoming increasingly popular due to their ability to handle complex data and identify non-linear relationships. Examples include neural networks and support vector machines.
#2 Qualitative methods
Expert opinions are gathered and iteratively refined through anonymous questionnaires to reach a consensus forecast.
Multiple scenarios are created based on different assumptions about the future, allowing for flexibility and contingency planning.
Surveys, focus groups, and competitor analysis provide insights into consumer behavior and market trends to inform demand forecasts.
#3 Hybrid methods
Combine qualitative and quantitative methods, leveraging the strengths of both. For example, using expert opinions to adjust quantitative model parameters.
💡How to choose a forecasting method?
Factor 1: Desired level of accuracy
Simple models may suffice for general trends, while complex models provide greater precision.
Factor 2: Data availability
More sophisticated models require large datasets for training.
Factor 3: Available resources
Building and maintaining complex models might require specialized skills and tools.
Factor 4: Desired level of detail
Some models focus on overall financial performance, while others provide granular forecasts for specific metrics.
Build forecasts with Bluecopa
Financial forecasting tools are powerful allies for businesses seeking to gain a competitive edge. Some advantages include:
- Enhanced accuracy and granularity
- Improved efficiency and automation
- Deeper insights and scenario exploration
By using financial forecasting software such as Bluecopa, your team can easily create, manage, and view financial forecasts, plans, and reports in real time. With Bluecopa, you could do—
- Quick and efficient cleaning and enrichment of data from various business applications
- Faster modeling of forecasts in just a few hours instead of days
- Immediate and on-demand running of what-if scenarios to aid in decision-making
To spend less time prepping data and more time forecasting, book a personalized demo.