The Current Remediation in handling the A/P Unpredictability

In order to absorb the unpredictability of accounts payables treasurers set aside a certain amount of cash (cash buffers) by either using cash reserves or by borrowing from financial institutions. 
This method of remediation further results in an increase in stagnant idle cash that cannot be used to generate any returns, and also increases costs, since borrowing (if opted as an option) comes with certain additional expenses tied to it. 
And if this method of remediation is considered, organizations may have to delay investments that may be crucial for an organization’s progress, since most of the cash is diverted towards making payments that are crucial for an organization’s sustainability.  With the current scale of how aggressive businesses are towards expansion, this method of remediation may prove detrimental rather than beneficial. 

AI-based Cash Forecasting Applications – The ‘Arsenal’ to Accurate Cash Forecasting

Considering the challenges and their expensive remedies it is imperative that the treasury function adopts a solution that can help treasurers in their initiatives related to cash forecasting. And from the many, one such holistic solution is Artificial Intelligence (AI). 
To the uninitiated, AI is a widely popular branch of computer science that builds solutions to intelligently automate key tasks that are heavily manual, error-prone, and taxes. In this context, for the treasurer, AI can automate the process of cash forecasting and help in improving cash & liquidity management for that forecast period.
To build an accurate cash forecast AI-based cash forecasting applications initially have to be integrated (which is done by the vendor offering the solution) with Cash Management systems, Billing Management Systems, ERPs, Treasury Management systems, and any other systems that house data related to cash inflows and outflows.
After integrating with these systems through an API, the AI based cash forecasting application fetches data automatically into its database. It is also important to note that at this phase the data is mostly unstructured and requires a further level of cleansing to make it structured, which is performed by the application, resulting in creating a structured database that has relationships mapped between most of the entities within data.
Upon structuring data, the AI-based application would then automatically choose the best-fit algorithm that can consider all the entities within the data and design a forecast that has the least variance and is extremely accurate.  
For instance, considering a use-case of building an Accounts Payable cash forecast, the AI-based cash forecasting system would fetch all the data related to payables into its database. And after fetching the data the AI-based cash forecasting application would then structure the data and run an algorithm that could accurately predict the payments that may arise in the forecasting period.

Deep Dive: How AI helps the Treasurer Predict the Payables of Future and Slash Forecast Inaccuracies.

After structuring the data, the AI-based cash forecasting application does a simple historical analysis that allows it to retrospectively identify trends and payment patterns. These trends and patterns would include data points related to:

• Historical instances of which forecasting period had sudden expenses and high variance, along with the identification of what the reasons were
• What were the categories of the payments that suddenly cropped up and the reasons behind it 
• Identify instances of late payments, and the associated penalties paid 
• Identification of payments made on time and the goods/services purchased on the purchase order
• Taxes paid during an accounting period
• Historical invoices booked during a certain period
• Investments made during the accounting period
• Repayments related to debt and identification of payment patterns 

Post the historical analysis the AI-based cash forecasting application would correlate historical data with the most recent data and perform a scenario analysis using multiple AI algorithms. The algorithm that presents the most favorable and realistic cash forecast is then chosen automatically to deliver an accurate A/P Forecast. 
This automated method of cash forecasting performed by the AI-based application helps the treasurer predict the expenses that may arise in the particular forecast period, foresee any particular fluctuations in costs and predict invoices that can be booked in a certain forecasting period. 
The treasurer is also at a benefit leveraging this information since he will be able to reduce the cash buffer, optimize cash and related assets, and improve the creditworthiness of the organization. 

Conclusion: How The Modern Treasurer stands to benefit from AI-based A/P cash forecasting

After structuring the data, the AI-based cash forecasting application does a simple historical analysis that allows it to retrospectively identify trends and payment patterns. These trends and patterns would include data points related to: 
It goes without saying that Cash forecasting is indeed crucial to an organization and its importance is only going to increase with time since businesses are always striving to progress, expand, and innovate. And at the center of these activities lies ‘cash’, so the more accurate a cash forecast is, the better is cash optimization and progress, which is only possible through new-age applications such as AI. 
Along with the benefit of cash forecasting accuracy AI-based cash forecasting applications augment a treasurer’s workday in the following ways 
 

• Improves financial management 
• Enables deep visibility into inflows and outflows
• Reduces the burden of maintaining excessive cash buffers
• Helps the treasurer provide strategic support to the CFO’s office