Machine Learning in Insurance: Fundamentals and Applications

Machine learning is transforming the insurance sector by automating claims processing, improving risk assessment, and refining pricing models. Understanding how data science applies to core insurance workflows gives analysts and business professionals a clear competitive edge. This course guides you through foundational concepts, key terminology, and practical machine learning applications tailored specifically to insurance operations. What you will learn: Learn foundational machine learning algorithms and core insurance industry terms; Analyze key insurance workflows including underwriting, claims handling, and customer retention; Explore predictive modeling techniques for risk scoring and premium calculation; Discover how fraud detection models identify suspicious patterns in claims data; Apply standard model evaluation metrics and modern data processing practices to insurance scenarios; Examine fairness, regulatory considerations, and transparency in algorithmic risk models. Course overview: You will start by mastering essential terminology and core insurance concepts before reading about popular machine learning models and frameworks. Through written explanations and practical case scenarios, you will build a solid working knowledge of data-driven insurance systems. Target audience: This course is designed for beginners, business analysts, and insurance professionals who want to understand AI applications without needing prior machine learning experience. Start reading today to learn how modern data science drives innovation in the insurance industry.