Top Terms in Insurance Analytics Fundamentals
What is it? The systematic use of data, statistical methods, and models to generate insights and information that support insurance decisions. When is it relevant? Across the insurance lifecycle, from marketing, sales and distribution, to underwriting and pricing, and through to claims management, portfolio management, and customer renewals. What does it do? Transforms data into information, insights and recommendations. What outcome does it create? More accurate, consistent, and profitable insurance decisions. Examples Using expected loss models to inform insurance pricing, or claims models to guide efficient claims handling. What is it? Analysis that summarizes historical and current insurance data. When is it relevant? During operational monitoring, reporting, and governance activities. What does it do? Establishes visibility into performance metrics and trends. What outcome does it create? A factual baseline for accountability and oversight, and a shared understanding of what has occurred. Examples Dashboards tracking claim frequency and loss ratio trends. What is it? Analysis that explains why outcomes occurred. When is it relevant? When performance deviates from expectations. What does it do? Identifies drivers, correlations, and root causes. What outcome does it create? Insight into performance variability. Examples Analyzing loss ratio deterioration by geography. What is it? Statistical and machine learning techniques that use patterns in the data to forecast likely future outcomes. When is it relevant? Before decisions are made, especially when foreknowledge of the outcome would affect the decision made. What does it do? Estimates probabilities and expected values of future events. It informs a decision-maker of what is more or less likely to happen. What outcome does it create? Reduced uncertainty and improved anticipation of risk and opportunity. Examples Predicting claim severity at first notice of loss. What is it? Analytics that recommends or automates actions based on predictions. When is it relevant? At the point of decision execution within insurance workflows. What does it do? Guides or automates optimal actions, reflecting corporate direction based on the predicted information. What outcome does it create? Improved decision execution and measurable performance gains. Examples Routing claims to fast-track or special investigation.