Okine, A. Nii-Armah. 2023. “Individual-Level Loss Reserving and Environmental Changes.” Variance 16 (1).
• Table 1. Estimation results for joint model for different sample sizes (number of claims).
• Table 2. Average of the mean (Mean) and standard deviation (SD) of settlement times and ultimate amount paid under steady state.
• Table 3. RBNS prediction results under steady state.
• Figure 1. Reserve distribution under a steady state and changes in environmental conditions.
• Table 4. Description of environmental changes and covariates used to implement changes.
• Table 5. Average of the mean (Mean) and standard deviation (SD) of settlement times and ultimate amount paid under change in underwriting practices.
• Table 6. RBNS prediction results under change in underwriting practices.
• Table 7. RBNS prediction results under change in underwriting practices (tightening underwriting criteria).
• Table 8. Average of the mean (Mean) and standard deviation (SD) of settlement times and ultimate amount paid under change in claims processing.
• Table 9. RBNS prediction results under change in claims processing.
• Table 10. Average of the mean (Mean) and standard deviation (SD) of settlement times and ultimate amount paid under change in product mix.
• Table 11. RBNS prediction results under change in product mix.
• Figure A1. Trending algorithms based on the partitioning of matrix for the individual development factors. Each partition is defined by the columns numbers on top and row numbers on the left.
• Code for Data Simulation, Estimation and Prediction.

## Abstract

The COVID-19 pandemic led to drastic changes in insurance operations. In general insurance, the pandemic led to an increase in claims in commercial business lines due to business interruption. It also led to a decrease in claims in personal automobile accidents due to reduced driving. In the loss reserving literature, changes such as those resulting from the pandemic that affects the insurer’s business and impact loss reserving are referred to as environmental changes. This paper underscores the importance of micro-level loss reserving methods, particularly when environmental conditions change, by identifying scenarios where the micro-level loss reserving methods outperform macro-level reserving methods. Further, we highlight the limitations of techniques in handling environmental changes under the macro-level reserving framework.

Accepted: March 29, 2022 EDT