Today, the insurance industry is in a highly regulated and competitive environment. Consumers pay premiums for getting insured, in exchange for the risk insurers take on their books. In pricing and underwriting, mathematical and statistical methods has been prevalent for a long time to appropriately price that risk (actuarial science). With rising expectations of the digital consumer, technological advancements, changing demographics, and highly unpredictable catastrophic and risky events, the need for differentiation has become more pressing. Insurance companies are transitioning from a “product-centric” to a “customer-focused” approach, much like the retail and telecom industries went through in the past. Moreover, since the advent of the internet, the exponential rise in different types of data has left many companies feeling like they are “extremely rich in data, but extremely poor in derived value”. This course will explain how executives and analytics practitioners can harness that data, and harvest the business value using analytics. After a brief overview of insurance industry, we will explain the different types of analytics. Subsequently, we will walk through various use cases to show how each line of business (LoB) could benefit from applying analytical techniques. We will then talk about the importance of a strong data foundation and organizational architectures needed to inculcate an “analytics-driven” culture within the enterprise. Finally, we will discuss innovations and trends that will fuel the need for analytics even more, and we will give some suggestions for companies that want to understand their consumer behavior better, and stay ahead in this competitive environment. Throughout the course, I will sprinkle real-world industry examples of analytical initiatives and challenges faced in delivering value.