Using the insights gained through the Maana Knowledge Platform regarding the root causes of late payments, the company created a customized call list for each collection agent; this call list was incorporated into the organization’s AvantGard GETPAID collection system.
As the Maana Knowledge Graph learns and adapts over time, it provides ongoing, data-driven recommendations regarding which customers should be called and when. For example, at one point, it recommended that accounts payable call all new customers ten days prior to their invoice due dates to ensure they understand their invoice and get questions answered.
After just 30 days of operationalizing recommendations like this one into GETPAID, the Maana Knowledge Graph and machine learning algorithm continues to learn, adjust and fine-tune insights and recommendations based on daily data input on open and closed invoices, collector actions and stock market changes. For example, the platform recently identified four groups of customers as consistently late payers, as well as specific strategies that the company can take to mitigate future late payments:
- First-time customers: The Maana Knowledge Platform recommended that finance make a courtesy call to these customers to ensure they understand their invoice and can ask questions at least 10 days prior to invoice due dates.
- Customers with unresolved service issues: The Maana Knowledge Platform recommended that the finance department call customer service to ensure open cases are resolved, as most customers with unresolved issues will not pay.
- Institutional Customers: These customers had contracts with longer net payment terms, so accelerating collections would require re-negotiating the contract terms.
- Other: Accounts receivable clerks should call all other customers with late payments for other reasons.
By operationalizing all of these recommendations, the company improved A/R collections by 65% over the prior year, which increased working capital by $520M per year.