Predictive Analytics for Invoice to Cash Collection collections worth $5.2M, through smart predictions & resource optimization




North America & Europe


End-to-end collections engine framework

Leveraging predictive analytics for invoice to cash collection helped optimize the use of collection resources and improved collection time by 1 week

Avoided the risk of late payment of invoices worth $5.2M per month on an average 

The Order-to-Cash (O2C) division of a leading CPG company wanted to improve cash inflow by transforming the invoice-to-cash collections process. Traditionally, as invoice-to-cash collections is a reactive process, the collection agents follow-up with customers only when an invoice is delayed beyond payment terms, or a dispute is raised. This approach is slow, manual and expensive.

The O2C division noticed that the delinquent invoice payments were directly affecting the EBITDA and cash inflow, and in turn leading to inconsistencies in financial stability.

TheMathCompany teamed up with the CPG company to take an intelligent and proactive approach by implementing predictive analytics for the invoice to cash collection process to improve cash inflows. The predictions were used to optimize collection resources and prioritize follow-ups of delinquent invoices, to avoid late collections and improve overall EBITDA.

We developed an end-to-end collections engine framework that utilised invoice, SKU and customer data to predict invoice delays, thereby enabling collections agents to take proactive action before an invoice is delayed beyond payment terms

  • An intuitive and forward-looking dashboard was developed to ensure prediction and performance tracking information was available in an easily consumable format
  • The dashboard was built in a modular manner and was scaled to 5 new geographies along with geography specific customizations
  • The modularity of solution led to low development time and reduced time to value for each new geography

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