Key Strategies for Treasury Teams to Manage Accounts Receivable Effectively

It’s always easy to attribute a company’s success to its sales figures.
More sales = More customers = More revenue.
But, as the saying goes, “Unnoticed goes the things you don’t want to notice!”
In the excitement around rising sales figures, many businesses increase their credit sales and ignore their accounts receivable. If the accounts receivable surpasses your credit sales, it could dry up your cash flow. Such scenarios occurred across a number of businesses during the pandemic and if lessons aren’t learned from them, businesses may struggle to cope with the recession which is almost upon us. Let’s take a closer look at the impact of market volatility on accounts receivable.

Effects of Market Volatility on Accounts Receivable

Accounts Receivable must be closely monitored because it is one of the leading indicators of a company’s cash flow and liquidity. Failure to track accounts receivable leads to a high DSO. This negatively impacts the financial situation.

Accounts Receivable are difficult to track due to the following two factors:

1. Seasonal trends: Seasonality is often difficult to factor in because each business has different peak and low sales times. The A/R process may slow down during holidays due to the unavailability of customers and this may affect your liquidity post-holidays. Another factor that can affect receivables is your customer’s cash flow when their business is seasonal or cyclical.
   For example, a company that sells electrical supplies may not experience significant seasonal fluctuations. However, that same company may have customers in retail construction that experience significant seasonal cash flow changes and fluctuations.
2. Customer payment behavior: Every customer has a different payment pattern and behavior. But during market volatility, payments might not come in the same historical manner. Spreadsheets are unable to monitor a variety of client actions and tendencies. As a result, A/R might not be acquired as expected. Not every macroeconomic factor affects customers in the same way, and not every customer behaves rationally to market changes, customers have varying capacities and willingness to pay. Hence, varying customer payment behavior makes the tracking of Accounts Receivable difficult.

These factors cause high unpredictability in receivables. Hence, companies must be more vigilant in tracking Accounts Receivables during market volatility.

Why is Forecasting Accounts Receivable Difficult?

Forecasting accounts receivable is always challenging as it is hard to predict when your customers will pay you. So, even if you raise an invoice on time and communicate the payment due date to your customer, you may not be completely certain whether your customer would make the payment. And, predicting the time when your customer will pay you could be as hard as trying to find a way out of a maze – blindfolded. This challenge is quadrupled in companies using manual AR methodologies.

We’ve listed down the challenges in forecasting accounts receivable:

• Difficult in short-term forecasting: Short-term accounts receivable forecasting is particularly challenging as it involves digging beyond general patterns. Companies need to decide whether or not clients can pay regardless of the invoice’s due date.
• Economic volatility: The main issue with longer-term Accounts Receivable forecasting is the unpredictable nature of market dynamics, which leads to inaccurate projections due to macroeconomic factors. Liquidity means the conversion of an asset into cash without affecting the asset’s market price. When macroeconomic conditions are considered, the value of the sale made on credit intended to be received can change because of currency fluctuations, international rules, economic volatility, etc.
• Limitations based on tools: A spreadsheet is the most used tool within the treasury department. The spreadsheet’s drawback is that it can only forecast accounts receivable using static forecasting methods, such as the ADP (average days to pay) method, which is often only 70% accurate.

Best Practices for Businesses to Manage Receivables

Accounts receivable is one of the drivers that helps in assessing the liquidity and financial health of a firm. Not keeping track of accounts receivable leads to high DSO, thus negatively impacting the financial status. In order to maintain sufficient liquidity, managing accounts receivable at a granular level is a proper way to go ahead.

Here are the two effective strategies to manage receivables:

• Monitor receivables: Track invoices and statements and forecast future cash flows using heuristic or AI models based on cash flow categories. Additionally, use the bottom-up approach by rolling up forecasts from local to global level to achieve granular cash flow visibility.
• Analyze customer payment behavior: Do a careful analysis of previous invoices and payment behavior of different customers to identify patterns that are likely to continue. Besides, use predictive analytics and incorporate multiple customer-specific variables in cash forecasts to understand payment patterns.

Apart from these techniques, companies should leverage AI to forecast their receivables strategically.

How Does AI Help to Create Accurate Accounts Receivable Cash Flow Projections?

Analytics is not a ‘nice to have’ but a ‘must have’ for businesses today, and it also empowers treasury departments to take proactive and thoughtful cash forecasting decisions. Data efficiency plays a crucial role for treasury teams because delays in data gathering could lead to incoherency in forecasting. The use of spreadsheets and ERPs contributes to such delays.
Hence, businesses can leverage AI to improve data accuracy and efficiency, enabling them to analyze cash flow patterns and action cash predictions accordingly.

Here is how AI in accounts receivable helps to generate accurate Accounts Receivable forecasts:

• Revises cash forecasts regularly: It helps forecast and re-forecast in real-time to obtain accurate cash flow projections.
• Provides granular visibility into Accounts Receivable cash flows: It helps track Accounts Receivable at regions, entities, companies, customers, and invoice levels.
• Accurately forecasts the payment date: It determines the timing of an invoice payment by analyzing customer payment trends.
• Analyzes historical data: It helps identify patterns likely to continue in the future by carefully analyzing previous Accounts Receivable forecasts.
• Helps in making strategic decisions: It helps to make confident decisions on debt, investments, business expansion, repatriation, etc., to utilize free cash better. This builds credibility for the treasury department.

Customer Success Story With HighRadius Cash Forecasting

A food & beverage company with a revenue of $25.3 billion leveraged A/R Forecasts with 96% accuracy to track open invoices of 1000+ customers and improve collections.

These were the challenges with the previous process:

1. Manually handled thousands of invoices resulting in an inaccurate Accounts Receivable forecast.
2. The current forecasting model allowed only short-term forecasting with a maximum horizon of 20 days.
3. Manually tracking customer and invoice-level payment status was time-consuming.
4. Customer-level discounts for early payments were hard to plan due to inadequate visibility, accuracy, and time.

HighRadius’ Cash Forecasting Cloud provided them with the following benefits:

1. 96% accuracy achieved within the first 30 days in Accounts Receivable forecasts.
2. Long-term forecasts are created automatically (6 months).
3. Automatically highlights variances in the Accounts Receivable forecast at a customer and invoice level.
4. Forecast and variance at daily, weekly, and monthly timescales are continuously analyzed across business unit AR cash flow, regions, and company codes.

Using the HighRadius artificial intelligence treasury software, the treasury team can reduce DSO by analyzing payment trends at the region, customer, and invoice levels. Get in touch with our solution expert today to improve A/R forecast accuracy up to 95%.