Cashflow Forecasting

Managing cash is a key issue for all organisations.  Many businesses have failed where profit and loss statements have shown healthy profits, but cash reserves dry up and creditors and employees cannot be paid on time.

Cashflow forecasting therefore, is a key requirement for all finance teams to ensure adequate cash reserves are available to meet debts as and when they fall due.  This is in fact, the definition of solvency.  Directors of businesses which trade insolvently are at risk of personal liability, significant fines and penalties, jail terms where dishonesty and recklessness can be proved and disqualification as directors by regulators.  Accordingly, regular and accurate reporting of cash positions and forecasts are essential for management teams, executives and board members alike.

The most common cash flow reporting tool used by organisations today is Microsoft Excel.  But like many other use cases for the spreadsheet software adopted by the world, Excel is not always the best tool for analysing and reporting on cash for all businesses.

Spreadsheets generally suffer from out of date information, errors in formulae and generally the effort to keep them up to date.  There must be a better way.

Integrated and comprehensive ERP finance systems provide a solution, whereby cash management features are provided within the accounting software.  Yet making predictions about future cash flows isn’t an easy exercise. 

The new world of artificial intelligence provides a solution in which machine learning algorithms are able to analyse historical data, continuously learn and identify new information as it becomes available and make accurate predictions about the future in ways that humans are unable to fathom.

Without AI, cash flow forecasts are likely to take into consideration only a small subset of the variables that can be used to identify trends, detect small nuances in data, and provide a complete picture of cash moving in and out of the organisation.

Millions of data points can be analysed by these AI models in minutes, resulting in far more accurate predictions than a person and a spreadsheet alone could manage.  And by incorporating cash flow forecasting within the accounting software, reports can be generated easily, that compare actual movements of cash against predictions and forecasts to determine how effective these systems are.

AI-enabled cash forecast prediction models also incorporate continuous learning algorithms that constantly learn from previous experiences.  Models are initially trained with sample data, but over time, become more and more accurate based on the actual cash payments and receipts being analysed by the system.

Modelling cash flows based on predictive analytics

There are many different types of transactions that provide valuable insights into potential cash movements within an organisation.  Future debtors and creditors transactions, such as receipts and payments based on due dates are an obvious source of predictive data, but how many customers actually pay their invoices on time?

Sales orders and purchase orders are often excluded from cash flow predictions, yet they provide a likely future transaction that will ultimately result in a movement of cash.

Being able to record known or expected future cash transactions is also an important input into the cash flow model.  This can be considered in terms of the realisation of asset sales, the purchase of assets based on capex budgets or raising of capital through a variety of cash generation activities.  A variety of methods for raising capital may also have an input, as do other budgeted cash transactions.

More sophisticated ERP systems provide for a cash flow ledger, that allows for the processing of journals in future periods that record expectations of significant cash flow movements without requiring entries in the general ledger, the record of historical transactions and accrual accounting entries.