To predict final settlement amounts for bodily injury claims open over two years based on store area and location, which analytics method should be used?

To predict final settlement amounts for bodily injury claims open over two years based on store area and location, which analytics method should be used?

• A. Regression analysis
• B. Time-series analysis
• C. Cluster analysis
• D. Qualitative risk assessment

Answer: (C)

Grouping claims data into clusters based on store area and location helps reveal patterns in settlement behavior that aren’t obvious when looking at all data together. Cluster analysis is an unsupervised technique that finds natural groupings where claims from stores with similar size and geographic factors tend to settle in similar ways. By dividing the data into these homogeneous groups, you can estimate typical final settlement amounts within each cluster and apply those cluster-specific estimates to new claims by assigning them to the closest cluster. This approach is useful when the relationship between store area, location, and final settlement varies across subpopulations, making a single global model less accurate. Other methods like regression would fit one equation to all data, time-series focuses on changes over time, and qualitative risk assessment isn’t quantitative, so clustering offers a practical way to segment and predict within segments.

Grouping claims data into clusters based on store area and location helps reveal patterns in settlement behavior that aren’t obvious when looking at all data together. Cluster analysis is an unsupervised technique that finds natural groupings where claims from stores with similar size and geographic factors tend to settle in similar ways. By dividing the data into these homogeneous groups, you can estimate typical final settlement amounts within each cluster and apply those cluster-specific estimates to new claims by assigning them to the closest cluster. This approach is useful when the relationship between store area, location, and final settlement varies across subpopulations, making a single global model less accurate. Other methods like regression would fit one equation to all data, time-series focuses on changes over time, and qualitative risk assessment isn’t quantitative, so clustering offers a practical way to segment and predict within segments.