Machine Learning for Cash Forecasting Is Here – Know the Top 5 Benefits

Written by Kalei White
December 5, 2022

Cash forecasts provide information that’s crucial to making smart business decisions.

Should you build up your reserves to service your debt? Or do you have sufficient capital to jump on a new business opportunity? Cash forecasts help you answer those questions.

The problem is that legacy technology—including spreadsheet applications like Excel—makes it challenging to produce good forecasts. Treasury analysts spend hours gathering and aggregating transaction data before generating forecasts using crude formulae, often based on error-riddled data.

Fortunately, modern cash management software solutions now come equipped with machine learning models that supercharge your cash forecasts. 

Here are the five top benefits of machine learning models for cash forecasting that will make you glad you upgraded from spreadsheets.

1. Better Forecasting Models

Perhaps the most significant benefit of machine learning models is that they’re extremely accurate—especially compared to spreadsheets. There are two reasons that machine learning produces better models.

First, the models are more complex. They include more data, both in type and quantity. With more input, the models can identify more intricate patterns in your cash management and better forecast your future cash position.

Second, they adjust over time. Machine learning models have the unique ability to use past performance to improve their accuracy. The longer you use them, the better their predictions get. 

As Antonio Chavez of the Biltmore Company said in a conversation with Trovata,

“Once you’re able to set up your bank portal with Trovata, the forecasts become very accurate, and I believe it’s because of the machine learning algorithms… the software does a really good job of forecasting inflows and outflows.”

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2. More and Better-Quality Data

Another reason that machine learning models produce more accurate forecasts is that they typically use more accurate data as input. 

Treasury analysts manually enter data into spreadsheets, often introducing unintentional errors.

One IBM report suggests that the great majority of spreadsheets—as many as 88%—include at least one mistake. And, of course, if the data in a spreadsheet isn’t correct, the forecasts won’t be either. 

In contrast, machine learning models rely on data you import directly from your bank accounts using an application programming interface (API). API banking eliminates manual data entry, delivering a single source of truth. 

Another drawback of spreadsheets is their data capacity. For example, Excel can only handle about a million rows of data. That sounds like a lot, but many companies have tens of thousands of monthly transactions across all their accounts. Spreadsheets simply can’t handle more than a few years of transaction data.

Cash flow management software has much greater data capacity. You can track transactions from hundreds of bank accounts for decades with no issues. Ultimately, that translates to more data for your models and even more accurate forecasts. 

3. Generate Forecasts Faster

Machine learning algorithms generate forecasts instantaneously—much faster than traditional spreadsheets. 

The boost in efficiency happens because your team automates data pulls from your bank’s API. No one has to manually log into banking portals, export data into spreadsheets, and then copy and paste data to consolidate it in the same file.

With open banking APIs, you can update your data and cash flow forecasts in real time. You save hours of work each month, and your team always has the updated numbers. That’s incredibly useful, especially in times of uncertainty when you need to stay nimble and make business decisions quickly. 

“During Covid, our main source of revenue was cut. The owners were keeping a very close eye on cash—even on a daily basis.”

— Antonio Chavez, the Biltmore Company

It also gives your treasury team the time they need to do higher-value strategy work. 

“Before, it was probably taking me a couple of hours a day to generate reports and forecasts. Now, it’s taking me about 30 minutes, which is not a lot of time. On average, we’re probably saving about 8 hours a week, which is, you know, a full work day.”

— Antonio Chavez, the Biltmore Company

4. Reduced Need for Highly-Skilled Analytics Staff

Cash management software lets you effortlessly create forecasts with beautiful visualizations based on keywords, filters, institutions, tags, or accounts in just a few clicks, rather than building each model by hand. 

With your analysis automated, your team members no longer have to spend time building analytics models. Instead, they can focus on drawing insights from the reports generated by the algorithms.

Shifting the burden of analytics onto machine learning algorithms means moving it off your treasury team. They don’t have to create the analyses. They just need to understand the results—a much easier task. 

The change in work means your team needs fewer technical skills. And that can be a substantial gift to your HR team: Rather than competing for a small number of highly skilled analysts, you can fill positions with smart, collaborative people that only need to understand what the forecast reports mean.

5. Validate Variance Between Forecasts

Forecasts change over time. That’s normal, but sometimes the difference between one forecast and the next is dramatic—and it can be challenging to know why. 

Machine learning models make it easier to understand variability between forecasts. They show both forecasted values and actuals, giving you visibility into variation. And when you know why your forecasts are shifting, you’ll feel confident about their accuracy. 

Steer Cash With Confidence

Machine learning algorithms take forecasting to the next level, offering faster and more accurate predictions than you could dream of with spreadsheets. These powerful algorithms provide unparalleled visibility into your future cash position so you can make informed business decisions. 

“The process we had [in Excel] was good, and it worked, but at the same time, it was taking quite a bit of time. That was one of the main reasons we decided to look for cash management software that would take our forecasting and reporting to the next level. Now, since all our bank data is flowing to Trovata, creating a report takes minutes instead of hours.”

— Antonio Chavez, the Biltmore Company

Trovata offers a cash management solution for finance, treasurers, accounting, and the C-suite that comes with machine-learning cash forecasting models built-in.

Looking to transcend the spreadsheet madness? Request a demo today!

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