Account receivables (AR) account for a significant portion of total assets and revenue. However, the account receivable process is typically handled manually by accountants or finance staff. This can lead to inefficiencies when it comes to identifying account issues and resolving them quickly. In addition, there are opportunities of leveraging data-driven decision making in different areas related to account receivables. In this blog post, you will learn about account receivables analytics use cases and how AI/machine learning and deep learning techniques can be used to streamline account receivable processes. For product managers and data scientists, this post will prove to be useful to understand different machine learning use cases related to AR processes.
What are key account receivables (AR) processes?
Account receivables processes help companies track account receivables and collect money from customers. The following are some of the key account receivables processes:
- Account reconciliation management: Account reconciliation is a process to ensure that account receivables match the financial records. This process can help companies identify account receivables problems such as duplicate invoices, incorrect billing addresses of customers, etc. Companies strive to automate account reconciliation activities by automating invoices & remittances information matching processes through one or more integrated SAAS-based software solutions. These systems are called cash application or cash reconciliation systems.
- Collections management: The collection management process in account receivables is an important task that helps businesses to keep track of outstanding payments and collect on them in a timely manner. This process generally involves four steps:
- Identification: First and foremost, businesses must identify which accounts receivable are overdue and need to be collected. This step includes reviewing invoices and determining which ones are past due. This is straight-forward and can be determined based on static rules.
- Assessment: Secondly, businesses must assess the situation of each overdue account and determine the best course of action for collection. This may involve contacting the customer directly via email or phone call and sending the account to a collection agency. This is a use case where AI / machine learning can be used to recommend most appropriate course of action which can result in collection. In addition, AI can as well be used to recommend the notification time duration before the collection date along with the frequency.
- Collection: Once a decision has been made on how to collect an overdue account, businesses must take action to actually collect the payment. This may involve making phone calls, writing letters, or taking legal action.
- Disposition: Finally, once an account has been collected, businesses must then dispose of it appropriately. This may involve filing away records or processing refunds if necessary. The challenges faced in collection management include timely payment of account receivables, customer relationships, and legal issues.
- Deduction management: Deduction management in account receivables is the process of tracking and managing customer deduction requests. This deduction can come from several places, such as a return, damage, short shipment, etc. The deduction request must be reviewed to see if it is valid and if so, the amount must be correctly applied to the customer’s account. This process can be difficult to manage manually, but this is where AI / machine learning comes to rescue. AI / machine learning classification models can be used to classify the deduction request as “Valid”, “Needs review”, “Invalid”, “Write-off” and drive decisions / actions appropriately. Deduction management can also help to improve cash flow, as it can lead to customers paying their invoices more quickly. An effective deduction management process can help to improve customer satisfaction and reduce deduction-related write-offs. In addition, it can also help to speed up the collections process by preventing deduction from becoming a reason for delayed payments. As a result, deduction management is an essential part of effective account receivables management.
- Credit management: Credit management helps companies determine credit limits for account receivables customers. The company uses a risk-based approach to decide whether or not to extend credit to an account receivable customer based on the likelihood of payment defaulting from that particular account receivable customer/debtor. AI / machine learning models can be used to predict risk associated with extending credit to an AR customer.
- Bad debt management: Bad debts are account receivables where it is unlikely that account receivables will be paid. Bad debt management processes help companies determine if account receivables are uncollectible or partially collectible by assessing the account receivable customer’s ability to pay and its willingness to pay, as well as collection efforts are undertaken. When customers default on account receivables, AR managers use bad debt recovery processes to recover some of the account receivables.
- Accounts receivable financing (ARF): ARF refers to a set of financial instruments which include accounts receivable factoring, account receivable purchase, or accounts receivables lines of credit. ARF helps companies to access cash immediately and reduces the need for short-term bank borrowings.
What are key machine learning use cases for automating different account receivables processes?
The following are few important machine learning use cases for automating some of the key account receivables processes:
- Classify whether deduction claim is valid or invalid: Machine learning classification models can be trained to classify account deduction claims as valid or invalid. This can help account receivables managers to improve the quality of deductions and reduce bad debts arising out of incorrect account deduction claims. Classical machine learning algorithms such as random forest could be used for this machine learning use case.
- Automated account reconciliation: Machine learning models can be trained to enable automatic reconciliation of account receivables invoices and remittance information from multiple connected sources such as ERP, accounts payable system, etc., which helps companies reduce account reconciliation manual efforts. OCR techniques along with classification machine learning models (random forest) can be used to match the invoice line items with remittance line items information.
- Predict customer creditworthiness: Machine learning models can help companies predict account receivable customer’s ability to pay and their willingness to pay by assessing the account receivable customer’s financial data. Classical machine learning algorithms such as random forest, gradient boosting machines (GBM), etc., can be used for this machine learning use case to predict account receivables customers’ creditworthiness. Based on account receivables customers’ creditworthiness, companies can decide whether or not to extend credit. This can also be used to determine whether or not to block the orders in case customers do not meet certain creditworthiness criteria.
- Estimate customer’s credit limit: Machine learning models can be used to estimate account receivable customer’s credit limits by assessing customers’ financial data and other risk factors of the account receivables customer such as bankruptcy.
- Payment date prediction: Predicting payment date can result in triggering of collection account receivables reminders and account receivable dunning letters to customers as well as provide account managers with a quick view of potential bad debts arising out of late payment. Machine learning models such as logistic regression, the random forest could be used for this machine learning use case.
- Ranking customers for collection processes: Machine learning models can help account receivables managers to rank their customers based on the risk of non-payment, so that high-risk customers are given priority for the account receivable dunning process.
- Predict account delinquency: Predicting account receivable delinquencies and early warning indicators of customer financial distress using classical machine learning algorithms such as random forest or GBM models has been a key area of research in the account debt collection domain.
- Detect account receivable fraud: Machine learning models can be used to detect account receivables fraud by identifying non-genuine transactions and accounts, which helps in increasing the security of the account assets as well as maximizing recoveries from fraudulent customers. Fraudulent transactions or account activities could be detected using machine learning techniques such as random forest along with account receivables account transaction-level data.
- Automated bad debt management processes: Machine learning classification models are useful in determining whether account receivables account balance is likely to become bad or not, so that account receivables managers can take early action for recovery of the account.
- Accounts consolidation: Identify customers having offices at different locations and consolidate account receivables account to the main office address. This helps companies save on account receivable efforts for managing multiple accounts.
- Customer segmentation: Machine learning models can help companies identify account receivables customers having similar characteristics, so that account managers could target them at the same time for account debt collection efforts or provide other personalized services to increase customer satisfaction levels with the organization.
- Predict churn probability of account receivables customers: Machine learning models can be used to predict account receivables customer churn and come up with strategies for account retention.
Machine learning is a powerful tool that account receivables (AR) managers can use to automate account receivable management processes. For example, machine learning classification models are useful in determining whether AR account balance is likely to become bad or not so AR managers can take early action for recovery of the account. Machine learning algorithms such as GBM and random forest could also be used by companies to predict customer creditworthiness, rank customers based on the risk of non-payment, detect fraudulence within accounts, estimate customer’s credit limits, and more. If you want help applying these principles to your own company’s unique situation please let us know!
I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. I would love to connect with you on Linkedin.
Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking.
Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking.
Latest posts by Ajitesh Kumar (see all)
- Agentic Reasoning Design Patterns in AI: Examples - October 18, 2024
- LLMs for Adaptive Learning & Personalized Education - October 8, 2024
- Sparse Mixture of Experts (MoE) Models: Examples - October 6, 2024
I found it very helpful. However the differences are not too understandable for me