ACA Predictive Analytics Report:
Examining 2021 Billing and Enrollment Data to Accurately Predict Terminations and Inform Retention Strategies

In this Data Report, Softheon shares member behavior indicative of future coverage lapses. Health plans will learn about the most influential factors for identifying members at-risk for coverage lapse and suggested retention strategies.

Softheon Report Executive Summary:

• To better understand ACA enrollment trends, Softheon created a predictive model using machine learning (ML) to analyze its 2021 billing and enrollment data, pinpointing key risk factors for individuals.

• The predictive model focused on the payment behaviors and enrollment data likely to result in a lapse due to non-payment, predicting member terminations with 80% or greater accuracy across multiple testing scenarios.

• This insight will provide health plans with early indications as to who might lapse on their health coverage and offer the opportunity to implement retention strategies for at-risk members.