4 Expert Tips To Level Up Your AR Forecasting

Introduction

Today, almost all businesses sell goods and services on credit, allowing customers to pay for their purchases at a later date. However, despite having solid collection strategies and favorable credit terms in place, businesses remain at  risk of customers not paying on time, leading to severe cash flow issues and accounts receivable (AR) aging.

The only way for businesses to address the cash flow problems arising from missed or late AR payments is to have robust accounts receivable forecasting in place, driven by state-of-the art AI. Businesses, with the help of AI, can leverage data like customer invoices, promise-to-pays, credit and debit memos directly from the ERP, rather than just relying solely on bank data and improve the accuracy of AR forecasts.

This blog will guide you through everything one needs to know about AI-led accounts receivable forecasting – what it is, why it is important, how to improve it, and how advanced cash forecasting software, helps businesses conduct accurate AR forecasting.

Leveraging AI for Accounts Receivable Forecasting

Accounts receivable forecasting refers to the process of estimating the expected future payments that a business will collect from its customers based on historical data, current market conditions, and business trends. It helps businesses manage cash flows, ramp up collections, and adjust payment terms accordingly.

An AI-led automated cash forecasting software like HighRadius, creates granular forecasts for AR cash flows, which is among the most difficult categories to forecast, enabling businesses to strengthen their cash flow strategies

Advanced cash forecasting solution also includes:

• AI-based forecasting of customer invoices 

Cash forecasting software uses advanced AI models to improve upon auto-machine learning prediction rates using customer invoice data from the ERP system.

• AI-based forecasting of sales orders 

Businesses can use advanced AI models to improve upon auto-ML forecasting for the time period beyond most open invoice dates using sales order data from the ERP. 

• Integrating collections promise-to-pays

Businesses can override predicted invoice payment dates based on customer-specific promises to pay, improving forecast accuracy.

For example, to improve AR cash flow forecasts, instead of just relying on bank data, HighRadius cash forecasting solution will pull invoice data from the ERP and predict account-specific payment patterns. This will create a better bottom-up estimate of cash flow from AR for the next 45 days.

Why Is Accounts Receivable Forecasting Using AI Important?

AR forecasting can help you identify potential problems before they escalate. For instance, if you notice that customers are taking longer than usual to pay their bills, you can take action to speed up the collection process and prevent cash flow problems down the line. Here’s how AI-driven accounts receivable forecasting helps streamline liquidity and cash flows:

• Improved AR cash inflow prediction

  outstanding invoices, AI-driven accounts receivable forecasts improve accuracy in predicting when payments will be received, leading to better cash flow estimates. Moreover, AI-driven forecasting of sales orders provides visibility beyond open invoices, allowing for proactive cash flow management.

• Tailored payment predictions 

  Businesses, with the help of AI, can adjust forecasted payment dates based on specific commitments from customers, enhancing the reliability of cash flow forecasts.

• Drive informed decision-making 

  Businesses get account-specific insights by leveraging detailed invoice data from the ERP. They also get a bottom-up estimate of cash from accounts receivable, improving decision-making for financial strategies and increasing the reliability of cash flow planning.

How To Forecast Accounts Receivables Using AI

Here are the steps to forecast accounts receivables using AI.

Step 1: Data Preparation

Begin by collecting historical AR data, including invoices, payment dates, and client profiles, ensuring the data is clean and normalized for analysis. Incorporate external factors like industry trends and seasonality, which can influence payment behavior.

Step 2: Feature Selection

Focus on critical predictors such as payment history (e.g., Days Sales Outstanding), invoice patterns like recurring or late payments, and customer segmentation based on size, risk, or industry. Consider seasonality, as certain times of the year may affect payment patterns.

Step 3: Model Selection

Choose an appropriate machine learning model based on your dataset’s complexity. Linear regression works for simpler trends, while Random Forest or RNNs are better suited for time-series data and more complex relationships.

Step 4: Model Training

Train your model by splitting the data into training and test sets. Use cross-validation to ensure the model generalizes well to new data, and fine-tune the model’s hyperparameters to optimize its accuracy.

Step 5: Predictive Analysis

Once trained, the model can forecast payment dates and predict when invoices will fall into AR aging buckets (30, 60, 90 days). These predictions provide a clear outlook on future cash flow.

Step 6: Performance Tracking

Track the model’s accuracy using metrics like Mean Absolute Error (MAE) or Root Mean Squared Error (RMSE). Continuously update the model with new data to maintain accuracy over time.

Step 7: Visualize & Act

Implement dashboards to visualize predicted payment timelines and identify potential risks, helping AR teams make informed decisions and take proactive steps.

Challenges Of Accounts Receivable Forecasting

Accounts receivable forecasting is difficult since it depends on the customers adhering to agreed-upon payment terms. This adds an element of uncertainty to the process. AR is considered as the most challenging category to forecast for treasurers.

Additionally, accounts receivable management becomes increasingly difficult as a company grows. For an enterprise, it becomes complicated due to scattered data across many business units. Without AI-driven cash forecasting, companies may face various issues, including poor receivables administration and time-consuming reporting processes. Here are some challenges businesses face with accounts receivables forecasting.

• Absence of AR expertise 

Most cash flow forecasting software does not offer any out-of-the-box solutions for accounts receivable forecasting. Without specialized capabilities in AR forecasting, businesses may struggle to achieve accurate AR payment predictions, leading to misinformed financial decisions and inefficient liquidity management.

Moreover, accounts receivable forecasting involves analyzing factors such as customer credit risk, historical payment behaviors, and market conditions. Lack of dedicated AR expertise in legacy forecasting solutions means they may not be able to capture these complexities effectively, resulting in less reliable forecasts.

HighRadius automated cash forecasting software focuses specifically on accounts receivable forecasting, leveraging advanced AI techniques tailored to these nuances. This specialization allows for deeper insights into payment behaviors and patterns, essential for accurate cash flow predictions.

• Inadequate data processing 

Older SaaS architectures in legacy cash forecasting tools are not designed to handle the large volumes of data generated by modern enterprises. This limits its ability to integrate and analyze extensive historical AR data quickly and efficiently. Moreover, these tools struggle with real-time data processing, which is crucial for accurate forecasting, leading to  reporting delays and a lack of agility in managing cash flow needs.

HighRadius’ robust cash forecasting software comes with a proprietary auto-machine learning system trained on historical transaction data to create cash forecasts. It selects the best fit and most accurate model from hundreds of combinations by category and time frame.

• Limited predictive analytics 

High volumes of AR transactions require the ability to perform frequent and detailed calculations to identify trends and forecast future cash flows accurately. Most of the legacy cash forecasting tools can only process limited datasets and cannot execute daily calculations effectively. Without the capability to analyze a broad dataset, these solutions miss opportunities to identify the most effective predictive models for AR.

HighRadius captures millions of critical AR data straight from ERP and performs daily forecasting computations. The cherry on top, our solution can determine the best predictive model for accounts receivable and increase the reliability and accuracy of cash inflow forecasting.

4 Expert Tips For Accurate Accounts Receivable Forecasting Using AI

Most businesses using legacy cash flow forecasting rely on  basic forecasting methods and often depend solely on  only bank data to predict accounts receivable payments. To improve AR cash flow forecasting accuracy, business need to leverage AI for AR forecasting that can process large historical datasets and implement best fit model

Solutions like HighRadius’ Advanced AI for AR forecast help businesses with deep expertise in accounts receivable forecasting and unlock the highest prediction accuracy. Here are four expert tips to ensure consistent accounts receivable forecasting and make the most of AI:

• Enabling customer-specific AR models

Building customer-specific AI models helps businesses improve accuracy by analyzing relevant data and enables them to develop reliable bottom-up estimates of cash flows for the next 45 days. By obtaining granular insights specific to each customer payment pattern, businesses can adjust their collection strategies accordingly, allowing for proactive cash flow management and the identification of potential late payments.

HighRadius’ Advanced AI Forecast for AR offers businesses customer-specific AI models built to improve upon the AutoML accuracy rate for the AR category. Depending on the availability and quality of data, these custom AI models are built by leveraging AR data like customer invoice information, sales order information, promise to pay, credit and debit memos, disputes raised, proof of delivery, deductions, etc.

• Build customer and invoice-level forecast reports.

By having a report that shows forecasted cash for individual customers and invoices, businesses can get granular visibility into expected cash flows and variances at the customer level. This level of detail allows them to assess the accuracy and feasibility of forecasts based on the quality of data, such as customer invoices, sales orders, promises to pay, and other relevant information.

HighRadius’ cash forecasting solution offers a customer-level forecast report that shows forecasted cash for individual customers. Businesses can view expected cash flow and variance at an individual customer level. The accuracy and feasibility of these forecasts depends upon the availability and quality of data such as customer invoice information, sales order information, promises to pay,credit memos,deductions, etc.

For example, a collections analyst might like to understand the cash forecasted from the top 20 customers for the next 60 days. This helps allocate collections resources accordingly and they get deeper visibility into AR cash inflows.

• No code platform for cash forecasting 

Many businesses still rely on spreadsheets to calculate cash forecasts and gather information manually. This not only makes data processing prone to errors but also makes the process slower. Creating user-specific forecasting units that combine cash categories and company codes helps businesses to foster collaborative forecasting, where accountants and collection analysts can manage their own forecasts, which are then integrated into a unified view.

With this in mind, our automated cash forecasting software brings a no-code platform for cash forecasting, LiveCube for cash forecasting. LiveCube offers Excel-like templates that connect human judgment and machine learning. On the backend, a cloud database stores millions of records and generates recurring forecasts automatically, allowing for manual adjustments in any cell, with time stamps for tracking and version control.

For example, if an analyst knows that a big customer payment is expected in Canada today, they can enter an adjustment to the AR cash category for Canada for today to include this big payment. This adjustment will show as a manual entry with a date and time stamp and can be adjusted in the future as needed.

• Enable average days to pay-based forecasting 

Average Days to Pay (ADP) is a metric that measures the average number of days it takes customers to pay their invoices. It provides insights into a business’s cash flow and the efficiency of its accounts receivable processes. Analyzing ADP improves cash flow forecasting by providing a nuanced understanding of payment behaviors linked to invoice amounts. Additionally, it supports the development of predictive models for future cash flows and informs strategic decision-making regarding credit extension and pricing structures, ultimately contributing to healthier cash flow and financial stability.

HighRadius’ cash forecasting software leverages the historical customer invoice data to come up with Average Days to Pay analysis at the individual invoice level to forecast cash For example, invoice amounts greater than $100,000 may have an ADP of 50 days, whereas if the invoice amount is less than $100,000, the ADP is 40 days. Therefore, even for the same customer, each invoice may have a different ADP, thus impacting the forecasted cash.

Boost Your Accounts Receivable Forecasting With HighRadius’ Cash Forecasting Software

Accurate accounts receivables forecasting goes beyond gathering numbers and financial data. Manual, conventional ways to process information, errors in projections, and loss of time due to dependence on various departments and counterparts hinder the effectiveness and efficiency of the financial forecasting process. To help businesses escape the chaos, HighRadius’Treasury Management Solutions offers advanced, automated cash forecasting software.

Our Cash Forecasting Software provides automated, custom-built forecasting models. These models are designed using Excel-like templates and are trained on historical data and heuristic models. In addition, these models offer out-of-the-box integration with all major banks that connect seamlessly with ERPs, gathering all the information required for accurate forecasting.

The cherry on top? The variance analysis and scenario analysis features. Variance Analysis ensures consistent and frictionless tracking of the cash forecasting outcomes while providing a comprehensive view of all past transactions and analyzing historical changes and variances over time. Scenario Analysis helps analyze the impact of each scenario on various cash flow categories, save the versions of those scenarios as  snapshots, and access them at any time in the future.

FAQ

• What are the benefits of utilizing artificial intelligence in AR collections?

AI in accounts receivables streamlines processes, improves accuracy, speeds up payments, reduces risks, provides insights, and enhances customer experience. By leveraging AI in accounts receivables, businesses can optimize financial operations and drive greater efficiency in managing receivables.

• What is generative AI in accounts receivable?

Generative AI enables businesses to build personalized collection strategies and collection letters tailored to match individual customer needs and preferences and payment behaviors. It can also automate repetitive manual tasks like sending invoices, applying cash, and collecting overdue payments.

• How is AI used in accounts receivable?

Advanced AI in AR can improve forecast accuracy by using data from ERP instead of relying on bank data. It uses invoices, P2Ps, sales orders, etc. for AR forecasts. For example, to forecast cash flows from AR in the US, invoice data is pulled from the ERP and predicts account-specific payment patterns. /p>