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Cash Can Be Predictable: Realising Real Time Finance

January 23, 2018

Cash coming into any business has always been important. However, if a Faster Payment means the remittance lags receipt of the cash, sometimes the cash can’t be released for use in the business - not ideal!

With predictive analytics, as part of an AI solution predicting payments and customer payments patterns, the system’s algorithm can do the allocation based on historical data. This means that predictive analytics can be used to forecast and predict future payments, providing a clearer view of what the cash looks like for next month.

It’s where intelligence starts to add real value to the business: the working capital model suddenly becomes automated and much more exact. You can go from a ‘finger in the air’ with a spreadsheet, to formulating concrete strategies on the back of real intelligence with solutions using predictive analytics.

Realising Real Time Finance

AI and RPA make it possible to take receipts from faster payments and match them to the right invoice and the right account and update accounts, customer records and ERP - all in real time.

It gives a real time view of the cash and, therefore, a real-time view of the debt. As a result, it’s possible to:

Collect against debt in real time
Update that debt in real time so the rest of the business can see what's happening
See promises to pay, we can see it in real time
Added to this, bank reconciliation is always in real time. Where, at present, bank reconciliation takes three or four days to close out, using AI and RPA it takes a few minutes to three hours.

In short, with AI and RPA, you have a real time view across cash, collections and bank records on what's happening in the business.

Predictive Analytics is Driving the Transformation of Credit

Debt collection traditionally targets the largest customer with the greatest overdue debt first.

This is changing...

Segmenting customers purely based on their volume of aged items currently forms part of a standard approach for many organisation. However, the introduction of new credit technology and processes (specifically the use of robotic process automation and predictive analytics), the increasing collection of data is more critical than ever.

By transforming information into intelligence, it’s possible to move away from the norm and begin to automate and improve processes. Using an inbuilt forecasting engine, based upon client behaviours amongst other things, new automation platforms can predict when customers are going to settle invoices.

New Credit Management solutions, such as Rimilia’s Alloc8 Collect, enable this step forward. Rather than having to navigate through endless data points to understand a debtor's true exposure, the data is maintained within a single location. This ensures complete receivables and this information can then be interrogated by the credit management team through a series of segmentation.

There are always sceptics to new technology

As with all new technologies and processes, there is scepticism in moving towards automation. In its early stages, people are unsure of automation and whether it will truly benefit them.

While for many credit professionals, the difficult and time-consuming task of completing a cash flow forecast is a lot less scientific, the pressure of achieving a cash target is now more important than ever.

However, initial results from our customers using automation is significant in convincing the sceptics, proving to them that it is possible to forecast payments with a significant degree of accuracy.

This ability to accurately predict payment behaviour has two profound benefits:

  1. Cash flow forecasts are provided with a greater degree of certainty and can be justified based upon actual data.
  2. The realignment of collections resources to target customers whose behaviour means that they are unforecastable.

Strategies driven by credit management software using predictive analytics can ensure value, risk, defaulted and new debt are covered. This approach has resulted in a greatly reduced level of bad debt, while showing an increase in values collected per customer contact for many companies.

For a long time, credit professionals haven’t been able to track accurately the value-added benefit that each call by their collections team has on the overall month end debtor position.

To actually make a difference, credit professionals have to be equipped with the right tools to drive real collection performance.