How AI Can Improve Financial Forecasting
Financial forecasting is an essential function within business planning, budgeting and operations management. Business leaders, investors and creditors review financial forecasts to assess projected revenues and expenses in order to estimate a company’s cash flow throughout the accounting period. A financial forecast considers trends in internal and external historical data and projects those trends in order to provide decision-makers with information about how the financial performance of the company is likely to be at some point in the future.
Forecasting is an important step at the outset of each accounting period because it establishes how the business will maintain the cash flow needed to cover its financial liabilities. It also provides data that leaders rely on when creating budgets. Additionally, financial forecasts heavily influence financial decisions about a major capital expense, hiring or other substantial investments. A valuable forecast indicates the resources needed, when they’re needed, and how you’re going to pay for these resources at some point in the future.
Emerging information technologies and artificial intelligence (AI) techniques are being used to improve the accuracy of forecasts and thus making a positive contribution to enhancing the bottom line. To improve their data insights, efficiency, and accuracy of their forecasts, finance professionals should be aware of how AI technology can address the disadvantages of traditional forecasting methods, and which FP&A software offer the most useful AI capabilities.
Growth & Demand Forecasting & How Traditional Methods Fall Short
Financial forecasting is the process of using time series data in order to estimate and predict future developments in areas such as sales, revenue and demand for resources and inventory. Specifically, financial forecasting is generally divided into two main categories:
1. Growth Forecasting:
Predictive growth modeling is a crucial part for more accurate corporate planning. In particular, with an accurate forecast of future growth, you can make better budgeting decisions, allocate resources more efficiently, and infer exactly what needs to be done to meet your targets. In order to conduct a growth forecast, FP&A professionals must ask the question: “What will be the revenue at the end of this quarter?” As you can imagine, revenue forecasting involves analyzing historical data using countless metrics such as geographic inventory, customer withdrawal patterns, etc.
2. Demand Forecasting:
Forecasting demand for resources such as inventory and staff is crucial for making sure demand is being met. An example of a demand forecast for a retail company would be asking the following question: “How should I plan the inventory needed for each location in the next quarter, month or week?” More accurate inventory planning for each location reduces inventory cost, and improves customer experience.
However, for both of these kinds of financial forecasting, traditional statistical modeling techniques like time series forecasting are inappropriate for handling the literal thousands of metrics and KPIs available that businesses typically deal with. You might be able to create a static forecast for the coming year based on a company’s 10 year revenue data with these older techniques, but they lack sufficient accuracy, and are unsuitable for the many more complex forecasting tasks organizations are up against in today’s corporate landscape.
AI Systems for FP&A & How It Can Address Traditional Techniques’ Shortcomings
There are three commonly used AI systems for financial forecasting:
1. Expert Systems:
Expert systems summarize the totality of available knowledge and rules. “Knowledge” is stored in a set of “if-then” rules. The knowledge base can be obtained by interviewing experts or integrating sets of data. They provide efficiency in quantitative analysis such as profitability, banking management, and strategic financial planning, and have successfully treated qualitative issues including financial analysis, investment advisories, and knowledge-based decision support systems.
2. Neural Nets:
This system emulates elements of the human cognitive process, especially the ability to recognize and learn patterns. The architecture consists of a large number of nodes that serve as calculators to process inputs and pass the results to other nodes in the network. Such networks have been proven to help finance teams automate increasingly complex processes and decisions that can ultimately lower costs, improve accuracy and customer experience, and give businesses a competitive edge.
3. Belief Networks:
Describe the database structure using a tree format. The nodes represent variables and the branches the conditional dependencies between variables. Belief nets generate conditional probabilities for a variety of future outcomes. For example, with belief nets you may estimate the chances of various product sales levels based on such traditional factors such as marketing and R&D budgets as well as market signals like customer complaints.
These AI systems can be employed for both forecast classification (e.g., preferred customer vs. marginal customer) and prediction (e.g., annual sales).
Having described the 3 types of of AI systems that finance professionals can use for forecasting, AI offers 3 main overarching benefits to financial forecasting:
AI finds correlations and patterns in data that humans and traditional forecasting methods never could find
AI can take thousands of metrics and consider each one for the prediction at hand to plan more accurately than manual methods
AI provides an autonomous turn key solution which continuously reconfigures projections as patterns change to better inform your decisions.
Product Options for AI
A finance team in any organization will substantially improve their financial data insights, efficiency, and accuracy through the integration of AI. There are a number of FP&A software products that possess AI capabilities that of course offer the aforementioned 3 benefits. Companies like CashWeb Community, Pureshare Activemetrics, and DataRails offer some of the best software with AI capabilities, aimed at helping finance professionals maximize their potential.