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.
Client was struggling with several key issues.
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.
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.
