“We had never worked with such accurate models. Machine Learning and predictive analytics is the way of the future.”
Healthcare │ Artificial Intelligence
In the health insurance industry, it can be extremely challenging to find useful indicators of unhappy customers outside of direct feedback from customer service calls or surveys. Yet, keeping customer churn as low as possible is extremely important, since the cost of acquiring new customers is steeper than retaining existing ones. Any improvement in customer churn has a big impact on revenue.
Identify members with a high probability of changing providers prior to the open enrollment period based on a limited dataset of claims and demographic data.
Obtain optimal classification results when working with imbalanced datasets resulting from a much lower number of disaffiliations compared to existing customers. Employ business acumen to balance a dataset by engineering new features without changing the real-life outcomes described by the original dataset.
We used Innovation Sprints to examine the quality of the data and determine the feasibility of creating a churn prediction model.
Initially we did not find any meaningful features to train a deep learning model given that all demographic and claims data followed the same distribution, so here is what we did:
❝We succeeded in creating a machine
learning algorithm with a 93% accuracy.❞