There are two main methods when it comes to cash flow forecasting.
Learn about when to use direct vs. indirect cash flow forecasting for your business.
Cash flow forecasting is a way to learn where a company stands in terms of its financial position by keeping track of the finances of a company and predicts where a company is heading.
Generally, there are two categories of cash flow forecasting techniques:
Direct cash forecasting or short-term forecasting shows cash positions at a specific time. It’s also called the receipts and disbursements method.
Time period: The direct method of cash forecasting is useful for up to around three months.
Inputs: It involves transactions like bills, invoices, and taxes.
Benefits: It predicts when payments will be made and when that amount will reflect in your bank account. For instance, it estimates when the payment will be received in hand, rather than the listed terms based on invoice date. It is built bottom-up by rolling up regional transaction data into a global forecast. This provides cash flow visibility at a granular level.
The most commonly used method for cash flow forecasting is the indirect method.
Time period: It is used for long-term forecasts, which range from one year to five years.
Inputs: It is conventionally used for longer-term planning purposes. It uses the pro forma balance sheet and profit and loss statements to predict cash flows including investments and financing. Ending cash balances are calculated based on adding back non-cash charges to net income and incorporating projected changes in balance sheet items.
Benefits: It shows the amount of cash required for expected business activities and helps in long-term expansion, repatriation, FP&A, and M&A planning.
To pick the most appropriate cash forecasting method and cash forecasting tools, you would need to analyze the size, mission, performance, and budget of your firm first.
Every firm starts with direct forecasting to keep a track of their daily cash movements on a frequent basis. Direct cash forecasting provides granular analysis and increased visibility and helps to:
Direct forecasting provides high accuracy in the short term. But as the complexity and volume of data increases, the indirect method becomes optimal. Big enterprises have more subsidiaries and more resources, so they might rely on the indirect method of forecasting for long-term funding and business growth. Indirect forecasting extracts data from existing reports and helps to:
Artificial intelligence is widely known for improving business processes and operations.
These are the top 5 reasons how it makes indirect and direct cash forecasting easy:
AI integrates readily with ERP, TMS, banks, payroll and tax systems, etc, and provides automated data aggregation.
Since all the data is stored in one place, it improves visibility and makes room for making smart decisions for using idle cash and increasing ROI.
Machine learning keeps evolving to improve the accuracy of the cash flow forecasts by including real-time data, which makes it more promising and dependable.
It provides a clear variance analysis globally and reduces the variance over time by studying previous results.
Risk management becomes easier through AI-based scenario planning which is done by tweaking some minor changes to the data in a spreadsheet.
Artificial Intelligence is progressing rapidly and is being adopted as an integral technology by many businesses since it is instrumental in reducing a great deal of effort and failures from the treasury realms, and yields significant productivity gain to treasury leaders.
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HighRadius Cash Forecasting Cloud – an advanced forecasting system – leverages the proven RivanaTM Artificial Intelligence (AI) platform to provide the most accurate cash flow forecasts – right from a ledger account level and rolling up to the organizational level. Delivered as a Software as a Service (SaaS), the solution seamlessly integrates with your company’s ERPs, accounting systems, banks and order management systems. Multiple AI and Machine Learning algorithms process datasets including bank statement inflows/outflows, sales orders/customers invoices, purchase orders/vendor invoices and expense reimbursements for comprehensive as well as accurate cash flow forecasts. The closed-loop, machine learning feedback system ensures that the forecast models become more accurate with time.