Cashflow Forecasting Systems : What’s New?

The importance of cashflow forecasting

According to a survey by Redbridge conducted in 2019, 94% of companies perform some form of cashflow forecasting. Most of the survey respondents noted that there are two key success factors to ensure high-quality and accurate cashflow forecasts:

Data quality

The importance of data quality is clear; without good data, a company will not be able to produce accurate cashflow forecasts that meet their needs.

Communication among contributors

The other key success factor may be less obvious, but communication among the various stakeholders in the company is just as critical for predicting future cashflows accurately. This work can become difficult and less productive if there are competing visions among departments, impacting the quality of the forecasts. It is therefore essential to get buy-in from all key parties in the process, or to automatically interface the cash forecasting production tool with the group’s various systems (e.g., CRM, EPR, credit management tool).

Despite the importance of cashflow forecasting for a company’s investment and financing decisions, a majority of companies still rely on a single technology – Excel spreadsheets – for this important task. However, spreadsheets have severe technological limitations and require a lot of manual effort. They are also susceptible to errors. The Covid-19 pandemic, and now the current political both highlight the need for companies to be able to produce different forecast simulations quickly. For this reason, treasury departments need to consider automating the cashflow forecasting process using more sophisticated tools and systems. However, automation remains a real challenge for many treasury departments, as this requires the selection of the systems and .

Overview of the main cashflow forecasting tools

In general, companies with an advanced solution use either a treasury management system (TMS) or a specialized cashflow tool to prepare their forecasts. Each has its own distinct advantages and disadvantages. For example, a TMS enables data centralization and saves times while improving the company’s cash culture. However, such systems might have limited reporting and analysis capabilities. They also require dedicated training to ensure optimal use.

A specialized cashflow tool provides advanced consolidation methods as well as advanced reporting. This can allow for a more extensive analysis and greater detail. It can also improve the company’s cash culture and optimizes internal processes. However, these tools also require an interface with the TMS or ERP and .

With the capability of more in-depth analyses, specialized tools parallel scenario management. For this reason, some companies, especially large conglomerates with a lot of subsidiaries, may choose this .

The impact of AI on forecasting

Artificial intelligence (AI) is becoming increasingly important in a number of fields, and cashflow forecasting is no exception. Both TMS and specialized cashflow forecasting tools are increasingly integrating AI into solutions. However, it is important to emphasize that AI is not intended to replace humans. Rather, it complements the work they do. Combining the power of AI with the critical vision that people provide results in cashflow forecasting modeling .

Artificial intelligence requires a lot of historical data to work well, both internal and exogenous. The data also needs to be very good quality, which involves cleaning it before it can be used and can be a time-consuming process. In addition, AI is not always transparent, which is another reason that can lead to hesitation when it comes to adopting AI. However, there have been great strides in terms of the tools that integrate AI.

Cashflow tools that use AI

On the TMS side, there are already several systems available on the market that use AI to forecast cashflows. These include Diapason, Kyriba, and Integrity, which add AI capabilities and algorithms to trending and modeling.

Another company offering AI-assisted tools is Coupa, which acquired Llamasoft and now offers machine learning solutions. These are based on an analysis of historical transactions, which are imported into the Coupa TMS and have been developed both for fraud management and to improve .

There are many more examples of TMS solutions using AI. However, a number of specialized forecasting tools involve artificial intelligence as well. These include:

• Agicap, which has developed a machine learning system for SMEs that enables daily cashflow forecasts based on actual cashflows
• Verteego, which applies AI to sales forecasts based on historical ERP data and then adds external data
• CashForce also provides an AI solution that involves machine learning
• TipCo is a European company that specializes in treasury reporting. They also offer a predictive analytics solution for automating cashflow

How to choose the right cashflow forecasting system & tools

Currently, a large number of companies rely on simple spreadsheets to determine their future cashflows. While Excel is cheap and easy to use with minimal user training, often there is only one person at the company who knows the file macros. This can lead to risks if that person isn’t there (because they are on vacation, or they’ve left the company).

If senior management wants weekly or bimonthly updates, automating the process to save time, reduce manual effort, and improve accuracy often results in the best cost-benefit ratio. Next, based on your particular environment, the quality of the data available within the group, and your reporting needs, you need to assess which methodology and tools provide you with the best return on investment.

Overall, finding the right solution or method depends primarily on your company’s size. Each option should be assessed thoroughly from a cost benefit perspective to allow you to choose the best one. If nothing on its own seems like a good fit, using a blend of different solutions or methodologies to create something specifically tailored to your company would be a necessity, as author Iris Rousselière points out in another article about cashflow forecasting. While best market practices are an ideal many strive for, you might need to complete a data management project first before you are able to fully unlock the true power of the cash forecast .