From Inaccuracy to Insight: How AI is Revolutionizing A/R Forecasting

Introduction

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:

• Inadequate time to carry out forecasts frequently
• Insufficiency in gathering information from the right sources
• Lack of variance analysis to understand the deviation between the forecasts and actuals
• Lack of granular visibility into unit-level forecasts

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.

Challenges Encountered in A/R Forecasting

The hurdles encountered by the treasury team while forecasting A/R are:

• Forecast inaccuracy due to limitations of spreadsheet: Using spreadsheets for A/R forecasting can lead to errors and inaccuracies due to manual data entry and limited data processing capabilities, resulting in incomplete data sets. Tracking changes in spreadsheets can also be difficult, leading to confusion and errors. Additionally, spreadsheets have limited reporting capabilities and lack automation, leading to delays, errors, and an increased risk of inaccurate A/R forecasting.

• High turnaround time in re-forecasting: A/R forecasting often involves forecasting cash flow for the next few weeks or months, leaving little time for detailed analysis. This can lead to errors or inaccuracies in A/R forecasts.
• Difficulty to gather data spread across ERPs, banks, FP&A tools: A/R forecasting relies on accurate data such as historical payment patterns, customer profiles, and sales data. However, inaccurate or incomplete data can lead to inaccurate forecasts.
• Lack of granular visibility of forecasts: Limited visibility into outstanding invoices, disputes, and payment trends can also make it challenging to create accurate A/R forecasts.

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.

Overcoming the challenges in A/R forecasting with artificial intelligence

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:

• Accurate prediction of payment dates for unpaid invoices: AI algorithms can be used to create predictive models that analyze historical payment patterns, customer profiles, and other data points to forecast future cash flow more accurately.
• Automated capturing of all the necessary financial data from TMS, banks, or spreadsheets: AI can be used to automate data collection from multiple sources, reducing the risk of errors and inaccuracies associated with manual data entry.
• Automated mapping of historical data with current data to analyze trends and patterns: AI can be used to analyze real-time data, such as receivables aging data, to identify changes in payment patterns and adjust forecasts accordingly.
• Ease in making frequent adjustments to the forecast: AI can generate automated reports that provide real-time insights into cash flow, allowing businesses to take proactive measures to manage cash flow more effectively.
• Enhanced data visualization: AI can be used to create interactive data visualizations that provide a clear and comprehensive view of accounts receivable performance, making it easier for businesses to identify areas that require attention.

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.