Insurance Forecasting & Predictive Analytics Training Course

Course Overview

Learning Objectives

• By the end of the course, participants will be able to:
• Explain the principles and applications of forecasting and predictive analytics in insurance.
• Analyze insurance data to identify trends, patterns, relationships, and emerging risks.
• Prepare and assess insurance datasets for forecasting and predictive modelling.
• Apply appropriate forecasting approaches to claims, premiums, revenues, and portfolio performance.
• Analyze claims frequency, severity, development patterns, and future loss expectations.
• Apply predictive analytics to underwriting, risk segmentation, claims, and customer management.
• Evaluate predictive model assumptions, accuracy, uncertainty, and limitations.
• Apply scenario analysis and sensitivity analysis to insurance forecasts.
• Identify potential data quality issues, bias, outliers, and model risk.
• Interpret predictive analytics outputs and translate them into practical business recommendations.
• Develop monitoring approaches for forecasting and predictive model performance.
• Strengthen data-driven decision-making across insurance functions.

Who Should Attend

Learning Outcomes

• Upon successful completion of the course, participants will be able to:
• Explain the role of forecasting and predictive analytics across the insurance value chain.
• Prepare and evaluate insurance datasets for forward-looking analysis.
• Identify trends, seasonality, patterns, relationships, and changes in insurance data.
• Develop and interpret forecasts for claims, premiums, revenues, losses, and portfolio performance.
• Analyze claim frequency, severity, development, and future loss patterns.
• Apply predictive analytics to underwriting and insurance risk segmentation.
• Use predictive insights to support claims management and early risk identification.
• Evaluate model accuracy, uncertainty, assumptions, and limitations.
• Conduct scenario, sensitivity, and stress analysis on insurance forecasts.
• Identify data quality problems, bias, outliers, and sources of model risk.
• Communicate predictive analytics findings clearly to technical and executive stakeholders.
• Establish monitoring approaches for forecasting and predictive model performance.
• Translate analytical results into practical recommendations for insurance management.
• Apply forecasting and predictive analytics to real-world insurance case studies.

Course Outline