Machine Learning in AR Software

Machine Learning (ML) is increasingly used in accounts receivable (AR) software to improve the effectiveness of B2B AR processing, especially collections.

Machine Learning is a branch of artificial intelligence that involves developing algorithms and models that enable computers to learn from data and make predictions or decisions without being explicitly programmed. For example, in accounts receivable, machine learning is used to automate and optimize the remittance application process, the collections process, deduction validation, matching credit memos, and cash forecasting.

Accounts receivable (AR) is the money owed to a business by its customers for the goods or services provided. Collecting customer payments and resolving customer deductions is a critical process for businesses as it ensures a steady cash flow, which is essential for their operations. However, collecting outstanding customer debts can be cumbersome and time-consuming, particularly in the business-to-business (B2B) sector, where invoices tend to be larger and more complex, with payments rife with deductions. 

In a B2B setting, the AR collection process typically involves several steps, from sending invoices to customers to following up with them for payments and then matching, investigating, and resolving deductions taken for a triad of factors. The traditional approach to collecting outstanding debts is manual and involves AR collections specialists manually reviewing and chasing invoices, disputes, and deductions. This slow and inefficient process lacks scalability and leads to accounts receivable write-offs.

Machine Learning For AR Vs. Configurable Rules

Using ML and Predictive analytics, historical data is analyzed to identify patterns and trends that can be used to predict future outcomes. For example, in AR collection, predictive analytics can predict which customers are most likely to pay on time and which are likely to be late or default on their payments.

To make these predictions, ML algorithms can analyze a wide range of data points, such as a customer’s payment history, credit score, industry, and geographic location. By combining this information with external data sources, such as economic indicators and market trends, ML algorithms can better predict a customer’s payment behavior. In addition, ML can automate many of the time-consuming and repetitive tasks involved in the AR collection process, such as sending reminders to customers, flagging overdue invoices, and prioritizing collection efforts. 

Not many companies see the ROI in making these advanced software investments in AR. In this case, intelligent, configurable rules using payment histories can accomplish the same results with less complication and cost. In this situation, customers’ payment histories can be used to determine the timing and sequence of collection actions. We can call this rational Intelligent Automation, where the system applies the data to define steps. For example, if a customer has been paying 45 days late, the system will generate collection actions (automatic emails, calls, etc.)  at whatever date you decide (say at 30 days?). If they have been paying 30 days late, the collection action is triggered at 15 days. If the invoice is over $50,000,  you may want a reminder email to go out five days before the invoice is due.

Using Natural Language Processing (NLP) in AR Systems

Another technology enhancement is using natural language processing (NLP) technology. NLP technology analyzes unstructured text data, such as emails, PDFs, and remittance backups, to extract meaningful insights. In this context, NLP technology can analyze customer communications to identify issues preventing them from paying their invoices. For example, NLP algorithms can identify common complaints or issues that customers may be experiencing with the products or services provided. By addressing these issues, businesses can improve customer satisfaction and reduce the likelihood of late or missed payments.

Additionally, NLP technology can automate customer communications, such as sending payment reminders and follow-up emails. By automating these communications, businesses can reduce the time and effort required to follow up with customers manually.

5 Reasons to Use Machine Learning and Smart Rules in Accounts Receivable Software

1. Predicting Payment Behavior: ML algorithms can analyze historical data on customer payment behavior to identify patterns and predict when and how much a customer is likely to pay. This can help collections teams prioritize their efforts and focus on customers most likely to pay.
2. Customizing Accounts receivable collection strategies: ML algorithms can also be used to analyze customer data and identify each customer’s most effective collection strategies. For example, some customers may respond better to phone calls, while others prefer email or text messages.
3. Identifying High-Risk Accounts: ML algorithms can analyze a variety of factors, such as payment history, credit scores, and other financial data, to identify accounts that are at high risk of becoming delinquent. This can help collections teams proactively address potential issues before they become more serious.
4. Automating Collections Processes: ML algorithms can automate routine collection tasks, such as sending reminders or following up with customers. This can help collections teams be more efficient and effective while reducing the risk of human error.
5. ML can improve financial operations by providing more accurate cash flow forecasting. Traditional AR software solutions typically rely on static assumptions about customer behavior and payment patterns, which can lead to inaccurate cash flow projections. ML algorithms can analyze historical payment data and identify trends and practices that are likely to continue in the future. This can allow businesses to make more accurate cash flow projections and better plan for future expenses and investments.

Machine learning is a powerful tool for accounts receivable software because it can help collections teams make more informed decisions, improve their collections strategies, and streamline their workflows.

In Conclusion

In conclusion, machine learning and the more straightforward Rule Configurator approach can potentially revolutionize how businesses manage their accounts receivable processes. By analyzing and acting on data and identifying patterns and trends, ML and rule algorithms can provide valuable insights into customer behavior, improve collections, and provide more accurate cash flow projections. In addition, as businesses continue to rely on technology to streamline their operations, machine learning in AR software will likely become increasingly common. As a result, machine learning can potentially revolutionize B2B accounts receivable management, including collections, dispute and deduction management, and invoice collections.