A major North American wholesaler, suffered from critical cash flow deficiencies caused by elevated DSO and rising late payments. Their collections department's reliance on manual, reactive methods prevented them from efficiently managing high-risk clients, leading to a backlog of unpaid invoices, numerous account holds, and legal disputes, ultimately impacting both customer relations and operational effectiveness

Challenges faced - Client encountered multiple roadblocks such as

• High past due value & increasing DSO - A significant number of invoices exceeded their due dates, leading to increased financial risk and impaired cash flow
• Inefficient & reactive collections approach - The absence of a structured prioritization system prevented the company from effectively targeting customers
• Frequent account stops & legal escalations - Auto-stopping a significant number of accounts and increased legal actions created operational inefficiencies, impacting both customer relations and revenue generation
• High volume of collection tasks & calls - The absence of a strategic predictive approach to collections resulted in agents spending excessive time on unprioritized tasks, generating a high volume of largely ineffective calls

Our solution: 

Our team identified key data points, like historical payment behavior, that influence payment likelihood and built pipelines to organize the final dataset in client's Azure environment. Then we implemented a ensemble of machine learning models-based Propensity to Pay Model allowing them to

• Take proactive actions
• Reduce dependency on manual collection efforts
• Minimize legal escalations and account auto-stops
• Redefine the auto account stops, order block rules

The propensities and other details were integrated with the ERP system for further workflow optimization.

Impact & Business Outcomes

The implementation of the Propensity to Pay model resulted in significant improvements in our client's collections process:

• 39% reduction in past due value – The past due percentage dropped from 16% to 10%, improving cash flow stability
• 15% reduction in weekly account stops – Proactive engagement prevented accounts from being placed on auto-stop
• 25% reduction in weekly collection tasks – Agents could now focus their efforts on accounts that truly needed attention
• 10% reduction in weekly call volumes – The targeted approach eliminated redundant follow-ups, reducing the workload on agents

By leveraging our AI-powered Propensity to Pay model, the client successfully shifted to proactive, data-driven collections.