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What’s the Future for Integrated Liquidity Planning and Forecasting?

What’s the Future for Integrated Liquidity Planning and Forecasting | Fintech Finance

You need to anticipate to avoid negative consequences

“Liquidity Planning allows companies to measure and test their short, medium and long-term liquidity based on different assumptions,” says Xavier Audibert, VP Product Architecture / Product Management at Kyriba.

It’s a key element in planning that starts with assumptions made by the business. Anticipating like this allows companies to optimize their cash and to prepare their financing and investments in terms of timing, scale, counterparty identification and currency hedging.

Forecasting is a challenge primarily related to data quality

To measure and anticipate your future liquidity, ideally you are continuously looking ahead. That means getting visibility over the coming year and even several years ahead, typically on a weekly or monthly basis.

“This exercise demands quality forecasts, so you need to have reliable, non-duplicated data. You also need to combine different sources (balance sheet, budget, account statements, etc.) then vary the assumptions according to the parameters. One of the biggest challenges is forecasting customer payments, since the due dates for accounts receivable are often theoretical (customers often pay later…),” explains Audibert.

This information is currently obtained using prior day and intra-day account statements, aggregated forecasts imported from ERPs (or alternatively imported details from individual AP/AR invoices), as well as purchase orders. This information then needs to be supplemented – if you want to extend the horizon of your forecast – with cash budgeting derived from accounting budgets and anticipated commercial operations by currency in order to manage foreign exchange risk. You also need to add in financing/investment schedules, available credit lines and overdrafts to calculate liquidity KPIs such as the “cash equivalent” item. It’s a challenge that traditional tools such as Excel can’t solve.

Integrated Liquidity Planning

The challenges of Liquidity Planning demand a greater volume of available data, which is then used in an integrated Liquidity Planning solution.

“Integrated means three things: better integration with ERPs, capitalizing on available yet fragmented data sources, and integrating artificial intelligence models to augment the data and make it more reliable,” says Audibert.

With regard to data sources he says: “most of the data you need is already available in Kyriba. We simply have to supplement it with medium/long-term cash forecasting.” Finally, you have to improve data processing in order to handle the greater volumes.

Artificial intelligence in aid of forecasting

In this context, artificial intelligence (AI) has a major role to play in processing large volumes of data.

“At Kyriba we already use AI in production through the Fraud Detection package in the payments workflow. It permits suspicious payment transactions to be identified automatically, using a scoring calculation determined in relation to payment history,” says Edouard Gabreau, VP Product, TMS at Kyriba.

“It’s a technology that we’ve mastered and that we’re rolling out, first to collection forecasting then eventually to other predictive models across all the Kyriba modules.”

The principle behind greater reliability in collection forecasting is simple: we start by training an artificial intelligence model through the use of historic invoices, relying on the date they were actually paid. That creates an artificial intelligence model. Next comes the prediction stage: each new invoice is analysed by the AI model, which makes a predictive calculation of the invoice collection date. The final step involves injecting those predictions back into the business processes.

The next edition of Liquidity Planning

For treasurers, the next edition of their Liquidity Planning will be a solution that incorporates multiple data sources, integrates customizable “What if” scenarios and is augmented by artificial intelligence.

It will intelligently combine several data sources clearly according to each budget line and period. It includes a data entry workflow or cash budgeting import workflow, with the version dependent on the company’s reforecasting process. It also includes customizable simulations.

“Integration of these solutions within a liquidity management platform allows you to manage the entire data chain, and ensure that the data is complete without re-entry. These predictive solutions will allow treasurers to anticipate and therefore to actively manage their liquidity better,” concludes Gabreau.

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