Overcome the bottlenecks in forecasting accounts receivable with AI cash forecasting

Why is it so challenging to accurately forecast A/R?

Forecasting accounts receivable presents a number of challenges for the following reasons:

• Data distributed over a wide range of sources
• Non-customizable and inaccurate tools
• Varied customer payment patterns
• Economic fluctuations
• The huge volume of invoices
• Number of entities involved

Factors causing unpredictability in forecasting accounts receivable:

• Different customer payment behavior/patterns
• Discounts and disputes
• Business cycles
• Seasonal trends

Why is forecasting accounts receivable accurately important?

The challenges associated with forecasting receivables using manually reduce forecast accuracy, which makes taking informed financial decisions cumbersome. Furthermore, erroneous estimates reduce the company’s credibility both within and outside of the organization. Hence, choosing the best process and tools for accurately predicting future A/R cash flows is critical.

Repercussions of inaccuracy in accounts receivable forecasting

• Poor inventory management
• Poor cash management
• Shortsighted business decisions
• Increased interest rates
• Decreased creditworthiness

How to improve accuracy in accounts receivable forecasting?

Some tips to help improve A/R cash flow forecasting are:

1. Historic data analysis: A thorough study of prior A/R forecasting can aid in the identification of trends that are likely to persist in the future. The following points should be highlighted in a complete study of historical A/R data:
   • Payment habits of the high-risk customers.
   • Problematic portions of the receivables ledger. For example, customers who consistently delay beyond payment terms.
   • Most volatile components of the receivables ledger. For example, certain business units, product lines, or customers whose payment times are variable and hard to predict.
   • Seasonality in the business, both an overview and detailed report.
2. Dividing accounts receivable into subcategories: Accounts receivable can be broken down further into sub-categories based on historical data analysis. The goal of the sub-categorization is to refocus improvement efforts on the error-prone areas.
   The subcategories can be:

• Size of the client
• Terms of payment
• Credit score of the customer
• Date of payment

• Monitoring and tweaking assumptions: It is critical to assess and alter assumptions on a regular basis to maximize accuracy in A/R cash flow forecasting. Using the ‘monitor, update, iterate’ process transforms a forecast from low to high quality within a short span of time.
• Using a fully automated A/R forecasting software: The largest benefit in terms of accuracy and time savings in the cash flow forecasting process, can only be obtained by using automated cash flow forecasting software. It will assist in automating the data collection, and in increasing accuracy.

Moreover, analytics helps find important patterns in financial data which is gathered from a variety of sources, both recent and historical, to create a picture of current outcomes and future projections. The results of analytics are derived from intensive mathematical modeling and computations using:

• Artificial intelligence (AI)
• Machine learning techniques (ML)

These technologies help comprehend cash flow patterns and make accurate cash flow forecasts.

Benefits of using AI in accounts receivable forecasting

Spreadsheet-based cash flow projections can only provide two-dimensional tabular reports on current period data from a single source. Whereas, AI cash forecasting gives a 360° view into present, historic, and future data, helping treasurers make confident business decisions. Here is how Artificial Intelligence helps treasurers improve the cash flow forecasting process:

Key benefits of using AI in forecasting accounts receivable

• Generation of accurate short and long term cash forecasts
• Comparing historical data of cash flows with the current forecasts to understand areas of improvement
• Analysis of different variables defining customer payment patterns using the best-fit algorithm
• 95% accuracy in prediction of payment dates

Schedule a demo to use AI cash forecasting software for increasing A/R forecasting accuracy up to 95%.