In my last post, we introduced this primer on insurance analytics by discussing the concepts behind descriptive analytics. This week, we am going to continue the discussion by exploring predictive analytics

Where descriptive analytics uses data to tell you what is happening, predictive analytics goes one step further and explores so what? Predictive analytics are used to derive actionable insight based on data from both internal and external sources. Data is analyzed using sophisticated software tools that leverage algorithms and programmed logic and rules to predict future outcomes.

Predictive analytics come in several forms. The first is statistical analysis, which tries to determine why something is happening by searching for patterns and trends in the data. Forecasting/extrapolation examines the data to determine what will happen in the future. Predictive modeling explores what will happen next, given past patterns and future plans. Optimization looks at the data and tries to determine what can be altered or tweaked to advance the best possible outcome.

Predictive analytics are extremely powerful and can be used by virtually every aspect of an organization, including:

  • Pricing
  • Long term planning
  • Marketing
  • Hiring talent
  • Assessing risk

In future posts, we will explore some real-world examples that showcase how insurers are leveraging the power of predictive analytics. Next week, we will explore the third category of advanced analytics, prescriptive analytics.

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