Predictive Analytics for Insurance Training Course

Course Overview

Learning Objectives

• By the end of this course, participants will be able to:
• Understand the principles and business value of predictive analytics in insurance.
• Analyze the insurance data requirements for predictive modeling.
• Prepare and assess historical insurance data for predictive analysis.
• Identify variables and patterns that influence insurance outcomes.
• Evaluate predictive modeling techniques suitable for insurance applications.
• Apply predictive analytics to underwriting and risk classification.
• Develop approaches for forecasting claims frequency and severity.
• Use predictive analytics to support insurance pricing and customer segmentation.
• Apply predictive techniques to identify potential fraud and unusual behavior.
• Analyze customer data to predict retention, lapse, and future behavior.
• Evaluate predictive model performance, accuracy, stability, and limitations.
• Identify model risk, bias, data quality, privacy, and governance issues.
• Translate predictive model outputs into actionable insurance decisions.
• Develop performance indicators for monitoring predictive models.
• Build a practical roadmap for implementing predictive analytics across insurance functions.

Who Should Attend

Learning Outcomes

• Upon completion of the course, participants will be able to:
• Explain the role of predictive analytics across the insurance value chain.
• Identify appropriate insurance data sources for predictive analysis.
• Assess data quality and determine its suitability for predictive modeling.
• Define insurance business problems that can be addressed through predictive analytics.
• Identify key variables and risk indicators within insurance datasets.
• Evaluate predictive modeling approaches for different insurance use cases.
• Develop predictive approaches for claims frequency and severity.
• Apply predictive analytics to underwriting and risk classification.
• Use predictive insights to support pricing and portfolio decisions.
• Identify potential fraud and unusual behavior through predictive techniques.
• Predict customer retention, lapse, and behavioral trends.
• Interpret predictive model outputs and communicate insights to management.
• Evaluate model performance and identify potential weaknesses.
• Establish controls for model risk, bias, privacy, and data governance.
• Develop a predictive analytics implementation roadmap for an insurance organization.

Course Outline