Three phases where AI improves budget forecasting

To determine a budget, follow its evolution and adapt it accordingly, it is now a shame to stick to a simple Excel table, we noted during the AI ​​for finance event.

Budget management on spreadsheets has had its day. Today the use ofartificial intelligence in the development and management of cash is a track favored by many companies, especially in finance. Evidenced by the place given to the subject at the AI ​​for Finance event on September 20 at the Palais Brongniart. With the approach of the transition to electronic invoicing from 2024, artificial intelligence and treasuries have every interest in getting along because the more electronic operations there are, the more data there is to analyze. The other reason is that APIs and instant payment have favored the rise of real time at the expense of cut-off time management, namely the segmentation of different accounting years. “AI solutions often fall under the analysis of trends to support human decision-making”, explains Pierre-Olivier Bouéedirector of financial services at Capgemini. In this transformation of the accounting profession, AI has its role to play. Or rather three…

In the short term

Optimizing the management of its cash flow, minimizing the amount of dormant liquidities while ensuring solvency, this is the first axis on which artificial intelligence is shaking up tradition. “Most accounting tables include sometimes erroneous data, yet the reliability of the data is the first brick of the financial wall”, observes Jean-Baptiste Gaudemetproduct manager of the treasury software Kyriba. Machine learning is a first solution and allows, thanks to historical data coupled with explanatory variables, to achieve an exact visibility in real time of its Treasury. “We first think of automation via RPA, to industrialize data collection, but we can also mention the use of standard solutions NLG (natural language generation, editor’s note) to facilitate, for example, the analysis or the generation of reports”, explains Pierre-Olivier Bouée. The automation of exercises for entering, processing and saving operations allows the accountant to concentrate on tasks with greater added value, while guaranteeing the reliability of the data he will have available.

The medium term

In a second step, artificial intelligence knows how to put itself at the service of the landing phases, these periods which precede the quarterly, half-yearly and annual reports. To anticipate available cash, make the necessary budgetary adjustments and stay within its medium-term objectives, the techniques of machine learning develop prediction rules coupled with BFR analysis software. This solution is particularly relevant in the analysis of the behavior of payers. “There is a whole family of algorithms to analyze and anticipate this kind of behavior and this is effective up to 90 days, that is to say the maximum period for payment of an invoice”, explains Jean-Baptiste Gaudemet. By digitizing the bills of a customer, the machine will analyze the behavior of the payer and determine from here when the invoice will be paid. Such medium-term visibility provides the company with a portfolio of quality invoices that can be decisive when negotiating its factoring rates or on the occasion of financing. Moreover, this contribution of artificial intelligence also makes it possible to avoid delinquent customers or customers with a credit risk by studying their behavior.

Liquidity planning

On a three-year horizon, machine learning is no longer enough for the simple reason that the past cannot guide the future. It is now a question of planning the financing needs of the company according to its growth objectives. Artificial intelligence makes it possible to anticipate the BFRs of the different scenarios through an analysis which makes it possible to understand how the business behaves according to the different hypotheses. AI software can integrate an algorithm and bring out hundreds of thousands of scenarios and carry out simulations such as a crisis case for example. A tool that would have made many companies happy during the first confinements when the incoming flows were for some dry.

By dint of data, this type of software makes it possible to present solid statistics to financiers by providing them with visibility on the company’s future liquidity. It is also better able to anticipate and plan its fundraising, particularly in debt.

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Three phases where AI improves budget forecasting

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