Managing receivables is a crucial aspect of B2B financial management. Late or unpaid invoices and bad debts can significantly impact cash flow, causing businesses to struggle to meet their financial obligations. Companies rely on various tools, including payment and credit scoring, to prioritize their B2B collection activities to stay on top of outstanding payments.
Payment history is an essential tool for managing B2B collections. It provides insight into customer behavior and can be used to prioritize collection efforts and focus on accounts most at risk of defaulting. For example, if a customer consistently pays late, it may be necessary to send reminders or follow up more frequently than with a customer who always pays on time.
Machine Learning (ML) 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. In accounts receivable, machine learning is used to automate and optimize the remittance application process, the collections process, deduction validation, matching debits to credit memos, and cash forecasting.
Advanced accounts receivable management software often uses Machine Learning (ML) to 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.
6 Reasons to Use Machine Learning in AR Software
- 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.
- 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.
- Customizing Collection Strategies: ML algorithms can also be used to analyze customer data and identify the most effective collection strategies for each individual customer. For example, some customers may respond better to phone calls, while others prefer email or text messages.
- 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.
- Collection Agencies: Determining when to place a past-due debtor account with a third-party collection agency to maximize the chances of recovery. The natural tendency is to delay an agency placement decision and avoid recognizing a loss. Paradoxically, the result of decision avoidance and waiting too long is the bad debt they were trying to avoid. Consequently, companies can benefit from automating placement through ML or even some simple system rules concerning what to do when a receivable reaches X age.
- Cash Forecasting: 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 patterns 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.
Using Natural Language Processing (NLP) in Accounts Receivable (AR) Software
An enhanced way that ML can improve the AR collection process is by 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.
In conclusion, machine learning has the potential to revolutionize the way businesses manage their accounts receivable processes. By analyzing large amounts of data and identifying patterns and trends, ML algorithms can provide valuable insights into customer behavior, improve collections, and provide more accurate cash flow projections. As businesses continue to rely on technology to streamline their operations, machine learning in AR software will likely become increasingly common. Machine learning can potentially revolutionize B2B accounts receivable processes, including collections, dispute and deduction management, and invoice collections.