A recent study of global corporations conducted by SunGard revealed a significant lack in the utilization of risk models to accurately prioritize collections for improved working capital. Despite advancements in technology and risk modeling, only 7% of companies are harnessing predictive analytics. This means that 93% of companies rely on a decades-old practice of sorting by age and value to prioritize collections, rather than an automated risk score, according to the 2014 study – click here to download the study.
Statistical-based credit scoring models are designed to predict the inherent risk of a customer, including their probability of delinquency and likelihood of filing for bankruptcy. A company can “quantify risk” through automated scoring models that take into account the specific customer payment behavior with that company. This enables A/R departments to develop collections strategies according to a proven and quantifiable risk score, rather than age or value.
“By using risk and predictive analytics, companies can save time while improving their cash flow. The study shows that 93% of companies are still leaving money on the table,” states C.J. Wimley, chief operating officer of SunGard’s AvantGard Receivables business. “The companies using predictive analytics to target accounts stand to benefit from more effective collection efforts and lower past due receivables.”
The study included over 400 participants across more than 20 primary industries.
