Tracking invoices and payments, sending reminders, following up on delinquent payments, and resolving disputes — managing account receivables (AR) is labor-intensive and challenging for any organization. Without the right tools and strategies in place, AR teams may struggle to keep up with the sheer amount of data and manual processes, leading to delays in payments, revenue losses, and unhappy customers.
This is where machine learning (ML) comes in as a game-changer for receivables management. With the right ML models, companies can make more accurate predictions about payment behavior, identify high-risk customers, and prioritize collections efforts.
In this article, we’ll explore how ML can transform AR management and help your organization stay ahead of the game.
Customize Collection Strategies
Machine learning algorithms can identify patterns in customer behavior, such as frequency of payments, amount of payments, and communication response rates. By analyzing this data, companies can better understand their customers’ payment habits and assess the likelihood of future payments.
ML models also analyze external factors, such as economic trends, industry benchmarks, and social media sentiment, to gain a more holistic view of customer risk. This can help companies adjust their collections strategies and credit terms based on broader market conditions.
AR teams can also use ML models, like decision trees and random forests, to segment customers based on risk profiles and develop targeted collections strategies for each segment. By analyzing these data points, ML algorithms can recommend the most appropriate course of action for each account — be it sending reminders, adjusting credit terms, or offering payment plans. This can help companies stay ahead of the curve and proactively manage customer risk.
Identify High-Risk Customers
Another way machine learning can be useful in AR management is through customer risk assessment. This involves analyzing customer data to determine the likelihood of delinquency or default and customizing strategies to reduce bad debt write-offs and improve collections efficiency.
Machine learning can create models that assess customer risk based on various factors — like payment history, creditworthiness, and industry benchmarks — to create customer risk scores. This helps companies to identify high-risk customers and adjust credit terms or collections strategies accordingly. For example, if a customer has a high-risk score, the company may adjust its credit terms to reduce the risk of default or implement a more suitable collections strategy to ensure timely payment.
Automate Communication
The dunning process is an essential part of accounts receivable management, as it involves sending reminders to customers with outstanding debts. However, manually sending reminders to every delinquent customer can be exhausting and resource-intensive. In fact, according to a Versapay report published in March 2022, 35% of businesses feel that properly communicating with their customers is the biggest challenge in collections. This is where machine learning comes in to automate the process and make it more efficient.
Companies can use ML algorithms to analyze customer payment behavior, communication preferences, and historical response rates to determine the optimal time and channel for dunning communications. For example, if a customer responds better to texts than emails, ML algorithms can send personalized text message reminders to them.
Further, you can also analyze the terminology and phrasing for more human-like and personalized sentences, improving the chances of response and payment. This can also help reduce the risk of customer churn, as customers are more likely to appreciate timely and personalized reminders rather than feel like they’re being hounded. Also, 78% of the c-level executives feel that better communication could have avoided many of their payment disputes.
So automating the entire flow of communication with machine learning can, in fact, make a huge difference to your collection process.
Detect Fraudulent Transactions
Fraud detection is a major time-consuming challenge in AR management. ML algorithms — like anomaly detection, clustering and decision trees — can analyze large volumes of transactional data to identify suspicious patterns and behavior.
These models can identify unusual patterns or outliers in transaction data, such as unexpected spikes in payment amounts or unusual payment times. ML algorithms can learn from historical data and identify these anomalies, allowing companies to investigate and address potential fraud. This can save time and resources for the organization while ensuring the security of the financial operations.
Automate Data Capture & Processing
Machine learning models can leverage optical character recognition (OCR) to automatically extract data from invoices, receipts, bank statements and other financial documents and input them into the respective systems. This can be a huge time-saver for AR teams, as it eliminates the need for manual data entry and the potential for errors that come with it. It can also help to ensure more accurate and timely processing of invoices, which can improve cash flow and minimize the risk of late or missed payments.
In addition, machine learning can classify and categorize invoices based on their content, making it easier for AR teams to prioritize and manage their workload. For example, ML algorithms can be trained to identify high-value invoices or those that require urgent attention, allowing AR teams to focus their resources where they are most needed.
Wrapping Up
Receivables management is a crucial part of any business’s financial performance. It’s also a challenge, however — one that often requires companies to implement time-consuming processes that can slow down the pace of collections and increase costs.
With the right ML models in place, businesses can leverage data to automatically identify and manage bad debtors, reduce manual processes and paperwork, and increase overall collections. By integrating machine learning into their AR management strategies, companies can improve the financial health of their organizations while reducing costs and improving efficiency.