New Mexico Department of Workforce Solutions Analytics 50 Submission
New Mexico Department of Workforce Solutions
In 2014, nearly one dollar out of eight distributed under Unemployment Insurance (UI) programs in the U.S. went to someone who was ineligible, resulting in over $4 billion of erroneous payments. Fraud and criminal schemes account for less than 5% of the total cost. In an effort to tackle the 95% of activity that results in improper payments, the New Mexico Department of Workforce Solutions (NMDWS) aimed to enhance program integrity, reduce overpayments without impacting eligible claimants and increase collection efforts.
In April 2015, NMDWS implemented the Improper Payment Prevention Initiative (IPPI), which combined insights from predictive analytics and behavioral science to successfully increase honest reporting while reducing improper payments.
NMDWS, in collaboration with Deloitte Consulting LLP, identified the key reasons, including individuals not performing required work searches and not properly reporting earned income while on benefits. The predictive model was developed based on patterns of past overpayments using predictive equations and suggests who is at a higher risk for an overpayment. By combining predictive analytics and behavioral science techniques, NMDWS implemented various forms of messaging, including certification boxes and pop-ups to remind claimants to review its information for accuracy and completeness, and a commitment mechanism when claimants log their work search activities.
Favorable results include: claimants who see a message are 40% less likely to commit fraud, and those who see the best-performing message are almost twice as likely to report earnings (avoiding an overpayment). By using the model, NMDWS investigators have been able to find 28% more overpayments with the same level of staff, and find them faster. People are getting back to work faster, too, with a 15% shorter time on benefits. These results add up to significant savings for New Mexico, without taking away benefits from eligible claimants or impacting staff time.