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.
Account receivables processes help companies track account receivables and collect money from customers. The following are some of the key account receivables processes:
The following are few important machine learning use cases for automating some of the key account receivables processes:
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!
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