The use of Artificial Intelligence and, in particular, its Machine Learning variant, contributes to the optimisation of liquidity by making it possible to execute cash flow inflows and outflows automatically and in real time.
Processes such as banking reconciliation, accounting entries, cash flow forecasting, cash pooling or a customisable system of alerts and notifications are some of the cases of corporate treasury automation that are revolutionising the way finance teams work. Process automation saves up to 75% of manual administrative work, as we will see.
To do this, it is necessary to operate via APIs. Thanks to the European PSD2 directive, the regulation allows third parties to access bank data via application programming interfaces connected between ERPs or other accounting systems and banks they work with. Moreover, this connectivity is bi-directional.
This access to cloud platforms makes it possible to optimise financial processes in a previously unknown way, as this access to data is in real time, which is the key to automating processes through software with artificial intelligence algorithms.
Currently, most companies do not carry out bank reconciliation in real time, but rather it is executed with a certain lag through bank files (Standard 43, Standard 19, MT940…).
Through APIs, by having bidirectional connectivity to send payment and collection activities to the ERPs instantaneously, it is possible to carry out this financial operation automatically.
In this way, once the outsourced platform has visibility of what's going to happen in different bank accounts of the company (collections and payments), when it detects such incomes and expenses it can identify and relate these movements with the forecasts of operations in the accounts and carry out automatic accounting of them in its ERP. This tool undoubtedly saves many hours of manual work of finance teams.
Within automatic reconciliation we can opt for partial reconciliation. At Embat, the product and engineering team has worked on the development of a recommendation algorithm based on machine learning techniques, capable of reconciliation by analysing history and recognising patterns of past behaviour
This pattern recognition is also used to carry out partial postings, to post a payment that groups several invoices together or an invoice that is collected in several payments, a financial task that has always been of considerable complexity in accounting.
The key to making these techniques work is to be able to give more depth to the data. For example, being able to extract data such as category, trade, payment type or invoice identifiers from a bank statement. With a greater number of parameters, the system will be able to identify patterns that previously could not be identified in order to perform such partial reconciliations in an automated way.
Through an accounting reconciliation engine supported by artificial intelligence and machine learning, automatic posting suggests accounting and reconciliation settlements, helping finance teams reduce manual task times, reduce accounting errors and avoid peak workloads.
Based on the development of advanced algorithms, systems are able to understand and interpret banking information autonomously, without human assistance, to determine, for example, what the cash position is in real time and in the future and to adjust or optimise it automatically. This process has its practical application in the creation of automatic cash flows.
Artificial Intelligence makes it possible to auto-categorise all the movements that occur in the different cash flow lines of the company, assigning each of the inflows and outflows to the items so that the system can create automatic cash flows without the need for human intervention. In other words, it frees finance teams from low-value administrative tasks that currently represent many hours of manual work.
Another benefit of data automation is the ability to visualise different economic scenarios. Based on certain parameters, it is possible to introduce variables into the solution that stage projections in such a way that the financial teams can foresee eventualities and take corrective measures in advance. Again, it is necessary to categorise the parameters so that the projected forecasts are as realistic as possible.
It is imperative to have active debt management in the face of the constant economic changes we are experiencing and thus be able to anticipate maturities that will lighten financial costs. The banking pool is a report that serves to take that picture, but it takes days to update it. Again, it is possible to automate it and have this portrait in real time when required. In addition to freeing up hours, it allows optimising liquidity management, which, in turn, leads to being able to negotiate better credit conditions.
As can be seen, the use of algorithms based on mathematical developments makes it possible to automate complex and tedious manual tasks. The most relevant aspect of these new possibilities is that they can all be carried out in real time. To date, companies working with bank statements have a certain lag in all these processes and financial operations, which they are unable to fully mitigate with the tools available on the market. This new system is sufficiently agile and capable of absorbing all the information it receives in real time to be able to launch the appropriate orders in each situation in a way that makes sense and create all the necessary automatisms.
