The accounting team at a global healthcare equipment manufacturer struggled to manage high volumes of accounts receivable, resulting in payment delays, tied-up working capital, and other inefficiencies. To solve these pain points, we adopted an MLOps strategy to create an autonomous accounts receivable solution.
First, our team gathered data containing more than 3.6 million invoices. Then, we built a customer-segmentation model to find variables influencing customer payment behaviors.
Through orchestrated pipelines, design patterns, and an MLOps framework, our AI solution could process more than 20,000 transactions in less than five minutes, identifying those customers at risk of missing a payment with 87% accuracy. Equipped with this information, teams at this Fortune 500 company could make decisions at speed, freeing up cash and strengthening working capital.