Generally, In a manual environment, the four core subprocesses are:
1. Data Gathering: It involves extracting data from multiple sources, country controllers, multiple departments, which is laborious.
2. Consolidation: Manually consolidating data into spreadsheets is time-intensive, cumbersome, and error-prone.
3. Decisions: Decisions based on long-term forecasts such as how much to draw on the revolver, or decisions on deleveraging to pay long-term debts at low-interest rates, or short-term forecasts to manage daily cash positions.
4. Variance Analysis: Forecast is compared to actuals, but it is performed usually for just one duration due to time constraints.
Manual-based cash forecasting is not just painstaking but comes with a lot of hurdles.
Despite most enterprises adopting a TMS, or a cash forecasting module, cash forecasting remains to be the biggest issue across industries.
According to the surveys at HighRadius, it has been noticed that Accounts Receivable and Accounts Payable are the toughest cash flow categories to forecast, but they are also the most important cash flows since they measure a firm’s revenue.
Since the forecasts have been inaccurate, treasury managers need to take remediation steps such as:
These challenges can be minimized by high margins by adopting robust and scalable technologies like Artificial Intelligence.
The AI-based approach simplifies cash forecasting subprocesses:
1. Data Gathering: Using a cash forecasting tool, data is extracted directly from the sources and systems, and presented as a single source of truth.
2. Consolidation: The system automatically consolidates data into regional and global views.
3. Decisions: Accurate data leads to accurate forecasts, which support finer decision-making.
4. Variance Analysis: Variance analysis can be carried out more frequently over multiple time horizons, hence performance can be measured frequently, and improved accordingly.
5. Scenario Analysis: Running scenario analysis is easier by changing multiple parameters and checking how they impact cash flows to take precautionary/ recovery steps in cases of distress.
AI-powered cash forecasting is not just time-saving but also foolproof as it guarantees a higher degree of accuracy.
With more forecast accuracy, richer reporting, and better decision-making through automation, the treasury manager is seen as a process specialist, who performs high-value tasks and is seen as a highly valued employee.
Technology transforms these KPIs into higher value KPIs that helps in closely keeping an eye on the future by handling manual tasks. The most common KPIs of a treasury manager involve:
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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.