This presentation focuses on the use of predictive analytics to gain a deeper understanding of insurance customers and enhance decision-making processes. It explores various components of predictive analytics, including demand modeling, internal models on loss costs, and projections of quote volumes based on macroeconomic and marketing factors. The presentation highlights how these predictive models can assist insurers in assessing and managing risk effectively. It discusses the application of data-driven techniques, such as machine learning and statistical modeling, to uncover insightful patterns and trends in customer behavior, preferences, and risk profiles. Attendees will gain a comprehensive understanding of how predictive analytics can empower insurance companies to make data-informed decisions, improve profitability, and deliver tailored products and services to meet the evolving needs of their customers.
Learning Objectives:
Assist insurers in assessing and managing risk effectively through predictive models.
Demonstrate how predictive analytics can empower insurance companies to make data-informed decisions and improve profitability.
Describe to company leadership how predictive analytics can help to gain a better understanding of risk selection within the underwriting function.
