Leveraging Accounts Receivable Predictive Analytics For Improved Cash Flow
Accounts Receivable Predictive Analytics
Using an accounts receivable predictive analytics solution, executives in the finance department can gain insights into their customers order to cash process. This data can be used to spot irregularities and payments that are late or delinquent, which can, in turn, lead to an improvement in cash flow. But, before an executive can make use of this analytics tool, they need to understand the process involved.
Step 1: Create Your Analytical Model
The first step in using an accounts receivable predictive analytics solution is to create an analytical model. This model should take into account all customer-based information, including credit iscores, payment history, and customer category information. By using all available customer-based data, executives can create stronger predictive model that will accurately reflect the customers current and future behaviors.
Step 2: Establish Credit Guidelines
Once the customer data has been collected, executives should set credit guidelines for customer purchases. By setting guidelines, executives can control the amount of credit available to customers and ensure that credit limit amounts are appropriate. This can help to reduce the risk of overextending credit and create more dependable cash flow.
Step 3: Monitor And Analyze Customer Data
Once the credit guidelines have been established, executives can begin to monitor and analyze customer data. By leveraging accounts receivable predictive analytics, executives can gain insights into customer behaviors and payment patterns. This data can then be used to spot abnormalities and delinquencies, which can help to improve cash flow.
Step 4: Utilize Automation
In addition to using predictive analytics, executives should consider using automation to streamline their customer order to cash process. Automation can provide variety of benefits, including reducing administrative coding errors and ensuring customer payments are processed timely and accurately.
Conclusion
Using an accounts receivable predictive analytics solution, executives in the finance department can gain insights into their customers order to cash process. By utilizing predictive analytics, creating credit guidelines, and utilizing automation, executives can create more secure and dependable cash flow. Through leveraging all aspects of the customer data and understanding customer behavior, executives can take advantage of an accounts receivable predictive analytics solution and improve their overall cash flow.
