Insurance Data Analytics Training Course

Course Overview

Learning Objectives

• By the end of this course, participants will be able to:
• Understand the principles and strategic importance of data analytics in insurance.
• Analyze the insurance data lifecycle and identify critical data requirements.
• Assess insurance data quality, completeness, consistency, and reliability.
• Apply descriptive and diagnostic analytics to insurance performance data.
• Use predictive analytics to support underwriting, claims, pricing, and risk decisions.
• Analyze customer and policy data to identify segments, trends, and behavioral patterns.
• Apply analytical techniques to identify unusual patterns and potential insurance fraud.
• Evaluate claims data to identify drivers of frequency, severity, cost, and processing performance.
• Develop meaningful insurance dashboards and performance indicators.
• Interpret analytical outputs and translate them into actionable business recommendations.
• Identify data governance, privacy, security, bias, and model-related risks.
• Evaluate analytical models and monitor their effectiveness over time.
• Strengthen data-driven decision-making across insurance functions.
• Develop a practical roadmap for expanding data analytics capabilities within an insurance organization.

Who Should Attend

Learning Outcomes

• Upon completion of the course, participants will be able to:
• Explain the role of data analytics across the insurance value chain.
• Identify critical data sources and analytical requirements for insurance functions.
• Assess the quality and reliability of insurance datasets.
• Structure insurance business problems into analytical questions.
• Apply descriptive analytics to understand insurance performance and trends.
• Use predictive analytics to support risk and business forecasting.
• Analyze underwriting data to identify risk patterns and performance drivers.
• Analyze claims data to understand frequency, severity, costs, and operational trends.
• Identify analytical indicators associated with potential insurance fraud.
• Segment customers and policies using relevant data characteristics.
• Develop meaningful dashboards and key performance indicators.
• Interpret analytical results and communicate insights to management.
• Identify data governance, privacy, security, and analytical model risks.
• Monitor analytical model performance and data quality over time.
• Develop a practical insurance data analytics roadmap.

Course Outline

Upcoming Dates