Case Study: Integrated Machine Learning achieves better predictive pricing and customer segmentation with Earnix

Integrated Machine Learning in Action

Integrated Machine Learning worked with **Earnix** to apply machine learning across banking and insurance use cases where traditional models were struggling, including missing data, customer product choice, cancellation risk, and competitive pricing. The challenge was to improve prediction accuracy and decision-making for offers, renewals, utilization, and pricing while handling complex customer behavior at scale.

Using **Earnix** machine learning capabilities such as random forests, gradient boosted trees, and deep-learning approaches, the organization built models to enrich customer data, predict product choice, optimize quote rank on comparison portals, segment customers for lifestyle programs, and forecast cancellation and actuarial costs. The results included more accurate renewal and product-choice predictions, better pricing and retention strategies, improved portfolio earnings, reduced negotiation effort, and the ability to more precisely tailor underwriting and pricing rules.