Cash forecasting is considered one of the top 3 priorities by enterprises, globally, according to Deloitte’s Global Treasury Survey (Nov 2022). However, 72% of finance leaders still forecast cash flow manually, as per PYMNTS. Manual cash forecasting can be extremely time consuming and highly inaccurate. Let’s take a look at some of the key factors that contribute to inaccuracy in cash forecasting are:
The greatest cause of cash forecasting inaccuracy is the unpredictability of accounts receivables. But what makes A/R forecasting challenging? Primarily, A/R involves inconsistent customer behavior, and in a volatile economy, it can be even more unpredictable with a high amount of past dues, making it a top priority for finance leaders.
The hurdles encountered by the treasury team while forecasting A/R are:
Adding relevant variables in cash flow forecasting improves the accuracy by a large margin. But, adding more variables increases the complexity. This makes it challenging to forecast A/R using spreadsheets due to human errors and consolidating data manually. However, the challenges due to spreadsheet-based forecasting are overcome by automated forecasting. With artificial intelligence, more variables can be added for greater forecast accuracy and help analysts shift from menial tasks to strategic tasks such as decision-making and reporting.
By leveraging AI in A/R forecasting, businesses can improve their accuracy and efficiency, optimize their cash flow, and make informed financial decisions. AI can help businesses identify potential cash shortfalls, take proactive measures to manage delinquent accounts, and ultimately improve their financial health. Artificial intelligence provides the following benefits in A/R forecasting:
With the help of AI, forecasts can be rolled up from regional levels to a global level for various entities, company codes, categories, subcategories, etc. You can invest in AI-powered A/R forecasting software that uses machine learning, natural language processing, and predictive analytics to provide accurate and comprehensive A/R forecasting. However, you must ensure necessary data infrastructure and expertise to support AI-powered A/R forecasting. This includes having access to high-quality data, implementing data governance policies, and ensuring that AI-powered solutions are integrated effectively with existing business processes.
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