Client overview

A leading wholesale distributor in North America, was encountering a rise in bad debt and a drop in sales, leading to financial pressure and operational difficulties. 

Challenges faced

Client was struggling with several key issues.

• Rising bad debt & credit losses - A growing number of clients were failing to make payments, resulting in considerable financial deficits
• Pressure from sales to relax credit policies - The sales team prioritized revenue growth, often pushing for looser credit terms to boost customer acquisition
• Lack of a data-driven credit decisioning system - Credit limits and payment terms were often set based on historical trends or manual judgment, rather than predictive analytics

Our solution

To address these challenges, we implemented an adaptive customizable Bad debt prediction model using advanced ML algorithms. Several data sources such as historical payment behaviour, financial data and customer risk factors were identified and engineered pipelines and incorporated in the model. Generated risk scores and learnings were taken into the strategic next steps for the risky accounts. This work laid a solid foundation for advanced sales analytics and planning targeted discount strategies. 

Impact & business outcomes

• 30% reduction in bad debt write-offs – Early risk detection prevented a substantial portion of credit losses
• 20% improvement in cash flow stability – Tighter risk management reduced overdue accounts and improved liquidity
• 40% decrease in high-risk credit approvals – Data-driven decision-making ensured that fewer risky customers received unmanaged credit extensions
• 15% reduction in sales vs. finance conflicts – Transparent credit terms facilitated better alignment between the two teams

Through the implementation of the AI/ML bad debt prediction model, our client shifted its credit risk management from a reactive process to a proactive strategy. By leveraging data-driven insights for credit decisions, our client proactively manages customer risk, facilitating controlled growth and safeguarding financial health.