Cash forecasting is meant to drive smarter business decisions in all economic climates. Amid the pandemic, it became even more critical for businesses to accurately assess and anticipate their future cash flows. Many finance teams —empowered by broader treasury digitization and technology breakthroughs — are now embracing new forecasting solutions that use machine learning to harness data and improve confidence.
To see how machine learning can help optimize cash, let’s first take a look at some of the traditional barriers to forecasting success.
What are the current challenges in cash forecasting?
Limited availability hampers accurate forecasts. All balances across different banks and held overseas, pending transactions and invoices must be aggregated and viewed holistically.
Lack of time and technology inhibits ability to analyze historical data and project a variety of economic scenarios. Spreadsheets and homegrown processes often lack built-in analytics.
Multiple bank relationships, where information is owned by different individuals regionally, and accessed only by logging into numerous portals to aggregate data into one place – impedes process to create a holistic forecast.
Manual effort is the result of addressing the challenges listed above. And the effort is significant, imprecise, and prone to human error.
So, what should you look for in a forecasting solution?
With a proliferation of new cash forecasting solutions available, the business should consider many factors. Does the business forecast at subsidiary and account level in addition to company level? By cash flow types? What are the integration requirements with other systems?
Start with a capabilities assessment. What is truly a “need” and what is “nice to have”. Evaluate a provider based on their ability to meet the needs first and the nice-to-haves second.
Take a look at built-in analytic tools. Even the most basic solution should have automated scenario planning , plug in growth rates and other assumptions. Likewise, evaluate the solution’s ability to integrate data from multiple sources and systems – providing a central place where it is easier to analyze.
No evaluation is complete without the assessment of implementation capabilities and support. Similarly, the solution must be easy to use. And of course the cost of a solution is critical. Factor in the maintenance fees and additional resources required for implementation in addition to purchase price.
What role does ML (Machine Learning) play in cash forecasting?
In this new era of cash forecasting, the increased prevalence of machine learning technology is on the rise. As a form of artificial intelligence, machine learning can help eliminate guesswork. Machine learning gets smarter as it interacts with more date. Automatically analyzing more transactions over time leads to improved accuracy and can help quantify variances to pinpoint where a forecast has gone wrong.
Machine learning is what is leading to treasury digitization and a new era of cash forecasting.
A solution like Bank of America’s CashPro® Forecasting, can help. With CashPro Forecasting, our machine learning does all of the heavy lifting – freeing up treasury staff for other activities – even from other banks – is collected in a single place.
Bank of America is one of the world’s largest financial institutions, serving individuals, small- and middle-market businesses and large corporations with a full range of banking, investing, asset management and other financial and risk management products and services. The company serves approximately 56 million U.S. consumer and small business relationships. It is among the world’s leading wealth management companies and is a global leader in corporate and investment banking and trading.