Overcome 3 Cash Forecasting Challenges
What you’ll learn
- Learn about the three performance levers of cash forecasting.
- Learn how AI helps to achieve the three levers to meet cash forecasting goals.
3 Key Cash Forecasting Performance Levers
There are three key performance levers of cash forecasting: 1. Visibility: Ability to view forecasts by categories, regions, and entities and track individual cash flows. 2. Accuracy: The efficacy of the forecasts required to make confident decisions. 3. Frequency: The forecasting cadence required to make timely decisions.Challenges in Cash Forecasting Performance Levers
Visibility
Since the data is scattered across several entities such as TMS, ERPs, bank portals, sales order systems, etc, it hinders visibility. Low visibility may also stem from non-standard processes, currency fluctuations, which lead to inaccurate forecasts.
Accuracy
Accuracy is affected due to:
1. Process inefficiencies such as:
- Top-down forecasting approach
- Unpredictability in A/R and A/P
- Disparate data sources
2. Dynamic factors such as:
- Changing FX rates
- Seasonality/ business cycles
- Macroeconomic fluctuations
Frequency
Due to the high turnaround time for generating forecasts, the frequency of forecasting is not timely enough.
The vicious cycle of cash forecasting causes data overload with a scope of error. CFOs have to rely on static and outdated data for making decisions.
How to Overcome the 3 Cash Forecasting Challenges
Visibility
Apply Direct Forecasting: Building local forecasts from the ground up at individual entity, company code, and currency levels, that ultimately roll up to central treasury. This provides transaction-level granularity.
Accuracy
- Use right models: Utilizing suitable models for each cash flow category. For instance, using heuristic models for repetitive cash flows like payroll, taxes, etc, and using M/L algorithms/AI models for complex cash flows like Accounts Receivable and Accounts Payable.
- Make adjustments: Factoring for one-time events like M&A, shares repurchase programs, dividends, etc.
- Analyze variance: Tracking variance for multiple time horizons and adjusting the models.
Frequency
- Determine the right forecasting cadence: Since each company has different priorities, it is important to identify the frequency that meets their business needs.
- Automate data gathering: Leveraging technology such as RPA and API to gather data in real-time from banks, TMS, ERP, FP&A tools
- Incorporating time-series algorithms: Prioritizing recent trends to proactively detect changes in behavior.
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HighRadius Integrated Receivables Software Platform is the world's only end-to-end accounts receivable software platform to lower DSO and bad-debt, automate cash posting, speed-up collections, and dispute resolution, and improve team productivity. It leverages RivanaTM Artificial Intelligence for Accounts Receivable to convert receivables faster and more effectively by using machine learning for accurate decision making across both credit and receivable processes and also enables suppliers to digitally connect with buyers via the radiusOneTM network, closing the loop from the supplier accounts receivable process to the buyer accounts payable process. Integrated Receivables have been divided into 6 distinct applications: Credit Software, EIPP Software, Cash Application Software, Deductions Software, Collections Software, and ERP Payment Gateway - covering the entire gamut of credit-to-cash.
