Financial Forecasting with AI
Let our unique AI automate the complex process behind financial statement forecasting.
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Multiple forecasting methods backed with AI
Forecasting has never been simpler! The financial statement projection features of SBB Financial Analyzer are the result of over 25 years of development and focus on financial statement analysis. Included forecasting methods range from highly automated quick forecasts to detailed forecasts providing item-by-item control. Forecasts for budgeting, scenario comparison and performance analysis are all covered. Stop skimming through miscellaneous financial forecasting templates and calculators – opt for automated forecasting that does the heavy lifting for you!
Forecasting financial statements has never been this easy! Up to four fiscal periods can be forecasted based on given turnover change assumptions. The financial artificial intelligence automatically identifies items dependent on operational volume, items generally remaining static, non-recurring items and items requiring custom calculation.
Automatic reconciliation is performed based on user-customizable default values for corporate tax rate, interest rate and minimum cash requirements. As a result, producing highly accurate forecasts is reduced to a single-click operation. Talk about a low learning curve!
A scenario forecast produces two to four alternative forecasts based on given turnover change assumptions. Forecast assumptions are, as the name implies, assumptions. Side-by-side forecasts covering e.g. pessimistic, expected and optimistic growth assumptions can be created by a single click to establish a range of financial outlooks for next fiscal period.
Detailed forecast allows the user to give individual change assumptions for almost any financial statement item. Expecting significant changes in personnel costs or external services? Need to evaluate the impacts of cost cuts? Is there a non-linear relationship between production volume and material costs? The detailed forecast feature handles these types of scenarios with ease.
Par analysis utilizes forecasting in an innovative way to analyze operational efficiency. The turnover change between two latest fiscal periods is used to create a reference forecast of the latest fiscal period. Profitability differences between the realized fiscal period and the reference forecast can be attributed to changes in operational efficiency. Root cause items can easily be detected based on the absolute and relative differences calculated for each item of the financial statement.