This process typically involves identifying the payment, matching it to the correct invoice, and updating the AR system accordingly.
Payments are received in various formats, including checks, electronic funds transfers (EFTs), credit card payments, and lockbox files. Automation tools can capture payment data from these various sources, including remittance information.
Automated algorithms match the payment information to open invoices in the AR system. This can be done based on invoice numbers, customer names, payment amounts, or other identifying criteria
Intelligent Document Processing (IDP) tools can extract relevant data from remittance advices, emails, or other associated documents. This data may include customer information, invoice numbers, payment amounts, and any deductions or adjustments.
In cases where an automatic match cannot be made, the system flags the payment for manual review. This can occur due to discrepancies in the payment amount, missing information, or other issues.
Once a payment is matched or manually reviewed, the system updates the AR system accordingly. This involves applying the payment to the correct invoice, reducing the outstanding balance, and updating the customer's account.
Automated cash application systems can generate reports on various metrics, such as the number of payments processed, the average processing time, and the number of exceptions. This data can be used to identify bottlenecks in the process and make improvements.
Cash application automation can significantly improve the efficiency and accuracy of the cash application process, reducing manual effort and errors, and improving cash flow visibility.
Automating repetitive tasks frees up staff to focus on more complex and value-added activities.
Reduces errors associated with manual data entry and matching.
Accelerates the application of payments, improving cash flow and reducing Days Sales Outstanding (DSO).
Provides real-time visibility into cash flow and outstanding receivables.
Reduces the risk of misapplied payments and improves customer service.
Easily adapts to increased payment volumes without requiring additional manual resources.
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Used to automate repetitive tasks, such as data entry and payment matching.
Used to extract data from unstructured documents, such as remittance advices.
Used to improve the accuracy of payment matching and exception handling over time.
Used to convert printed or handwritten text into machine-readable data.
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