Financial Forecasting

Financial forecasting is a method of predicting future financial events of a company or organization based on historical data. The purpose of a financial forecast is to make informed decisions regarding your finances and to increase the financial stability of an organization. These can be prepared for both short and long term periods.

When conducting a financial forecasting analysis, various factors are considered, such as historical financial data and trends, macroeconomic factors such as interest rates and inflation, and industry-specific developments and trends.
However, there are also potential challenges in conducting financial forecasting, such as unforeseen events that can affect forecasts and the limited accuracy of forecasts.

Overall, financial forecasting is an important process for companies and organizations to forecast future financial events and make informed decisions that can contribute to financial stability and growth.

Key financial figures serve as decision support, but forecasting them often resembles looking into a crystal ball. In increasingly globalized and volatile markets, planning with the established forecasting methods, whether of sales, revenue, liquidity or other planning variables, is becoming more and more difficult and imprecise. At the same time, accurate forecasting of planning variables is more important than ever!

Accurate forecasting not only offers competitive advantages, but is also essential for companies to survive. With Financial Forecasting, we offer you the opportunity to increase your planning accuracy and to make your planning processes more efficient.

Predictive analytics models can be used to create forecasts for resources and key figures in a wide range of business areas. Use planning metrics and key performance indicators across departments and leverage them across the enterprise to optimize processes.

Various time series analysis and machine learning models are used in forecasting key financial figures. While classic forecasting methods focus on internal data and budgeted figures, powerful predictive analytics models can be used to take into account a wide range of internal and external drivers in addition to historical data. In addition, they can be used to better map interactions between the various influencing variables, which is where classic forecast methods quickly reach their limits.

Thanks to the learning ability of the algorithms used, forecasts become increasingly accurate over time. The numerous, time-consuming manual planning activities are also a thing of the past with the use of predictive analytics models. The intelligent models independently identify trends, seasonalities and patterns in historical data and use them to create forecasts. Based on the forecasted key figures, subsequent processes in the company can be sustainably optimized. Use your liquidity forecast to improve your overall liquidity planning and financing strategy.