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Enhance Treasury Management with Machine Learning and Artificial Intelligence

Written by Keegan Chamberlin
July 13, 2022

Treasurers face challenges today due to outdated, manual treasury management methods. However, with our increasingly digital world comes the potential for new solutions. Leaps in technology have given treasurers a chance to rethink the status quo and take big steps toward agile cash management. 

Two of these efficiency-driving advancements are machine learning (ML) and artificial intelligence (AI) algorithms.

But first, what is the status quo of treasury management and why is it such a resource drain? 

Treasury management best practices


The Status Quo of Treasury Management

We’ve reached a point where treasury management’s status quo is no longer feasible for growth in the long term. Typical workflows are labor-intensive, requiring treasurers to download data across many bank portals. This forces organizations to normalize and format data in spreadsheets manually. This manual process becomes more time-consuming as your organization scales.

Building cash reports and forecasts manually results in excessive hours spent wrangling data instead of analyzing it.


Leveraging Machine Learning and Artificial Intelligence

Your organization can gain better cash visibility by utilizing ML and AI. ML helps companies analyze historical data for data patterns to discover new insights.

There are some limitations to ML, however.

If your bank data is not up to date or accurate, you may experience the phenomenon we call GIGO (garbage in, garbage out).

The insights gained from ML are only as good as the data it analyzes. Bad data fed into ML result in nothing more than models featuring insufficient data.

With access to accurate data, ML algorithms can spot trends that humans simply can’t. In addition, ML iterates and improves the more data it analyzes. As a result, it becomes more intelligent over time. This advancement can help your treasury team make quicker strategic decisions. 

So, what are some of the functions that ML incorporates to achieve these results?

Let’s look at how Trovata integrates ML and AI into its cash reporting and forecasting capabilities.


Benefits of Machine Learning: Cash Reporting

Banking APIs work in tandem with ML to aggregate bank data from multiple portals. ML analyzes this data as it enters Trovata, standardizing and normalizing it. This enables you to view the data across all accounts, balances, and transactions.

Tagging capabilities can organize cash inflows and outflows into specific datasets, enabling your treasury to search and filter transactions across key vendors and institutions. In addition, these tags can be leveraged for future cash reporting and forecasting. 


Benefits of Machine Learning: Cash Forecasting

You can leverage ML for forecasting as well to establish a forecast baseline. ML analyzes historical data to help forecast potential future results. This historical data, augmented with known events, can give a complete picture of your organization’s health.

ML and AI algorithms help spot trends to cyclically improve over time to make more accurate predictions for the future. Humans can apply assumptions and scenarios to establish cash management contingency plans. These plans can be implemented when certain market conditions arise.

The future looks bright for the application of machine learning within treasury management. But machine learning can’t improve without people. While machine learning can automate some forecasting functions, it does requires some manual intervention.


How to Improve The Accuracy of Your Cash Forecasts

While ML and AI significantly improve forecasting accuracy, you should still consider these best practices.

  • Automate Your Starting Point With Open Banking APIs. What often prevents treasurers from performing more meaningful analysis is time-consuming data management. Trovata’s open banking APIs establish rails that aggregate historical bank data into a single platform.

  • Augment Historical Data With Known Events From Key Stakeholders. Your organization’s historical bank data doesn’t tell your entire cash story. You can increase your forecast accuracy by incorporating leadership in the forecasting process. This can help your team make more data-backed decisions.

  • Maintain a Forecast Baseline to Perform Accurate Variance Analysis. Automated cash management platforms save and maintain your baseline forecast for variance analysis. With variance analysis built-in, you can determine your forecasts’ accuracy for any period.

  • Focus on Discovering Overall Trends Within Your Data. A practical forecast enables your team to adjust your cash strategy accordingly. By focusing on overall trends within your bank data, you can discover opportunities that could dramatically impact cash.

  • Regularly Review Your Assumptions to Optimize Your Forecast. The market is constantly changing, which can facilitate the need for strategy changes. Communicate with key stakeholders to understand when these events could occur. This can enable you to make your forecast more agile.


Enhance Treasury Management with Trovata

Trovata makes it easier to generate a 13-week forecast with its suite of cash reporting and forecasting functionality. In addition, automated forecasts account for historical data trends, increasing forecast accuracy.

Download our guide, Top 10 Cash Forecasting Best Practices to Improve Accuracy, to learn how your organization can discover richer cash insights through incorporating these cash forecasting best practices.

Treasury management best practices


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