Treasurers face challenges today due to outdated, manual treasury management methods.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. When used the right way, treasurers can use them to boost the company’s overall profitability, something the CFO will appreciate.
But first, what is the status quo of treasury management and why is it such a resource drain?
Contents
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 cash management workflows are labor-intensive, requiring treasurers to download data across many bank portals just to get a handle on cash flow. This forces organizations to normalize and format data in spreadsheets manually. This manual process becomes more time-consuming as your organization scales.
Even if you have a treasury management system, there’s a good chance it runs on twenty-year-old technology, with weak connectivity to your banks, ERP, and financial services.
Treasury departments that build cash reports and cash flow forecasts manually spend excessive hours wrangling data instead of analyzing it. Paired with access to real-time data, the use of AI becomes a true game changer.
Leveraging Machine Learning and Artificial Intelligence
Your organization can gain better cash visibility and a competitive advantage by utilizing AI technology. AI 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). This means, you won’t have great information for effective decision-making and risk management.
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, a fintech company bridging the gaps between banks and corporate treasury, integrates AI technology 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, especially with the introduction of new technologies like ChatGPT. But machine learning can’t improve without people. While machine learning can automate some forecasting functions, it does requires some manual intervention.
Learn How Trovata AI Can Help You Generate Cash Forecasts
Trovata AI is the world’s first generative AI for finance and treasury teams. Check out this live stream replay where Trovata CTO and CPO, Joseph Drambarean, explains how to use it in the forecasting process as well as use it to answer all your cash flow questions.
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 allow you to ditch Excel spreadsheets while aggregating 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, “How to Prepare Your Finance Team for AI & Get Started Now“, to learn how your organization can get started with the latest and greatest in AI, like Trovata’s generative AI tool.
