Fundamentals of General Insurance Actuarial Analysis Appendices

Introduction

In general insurance, actuarial analysis provides the quantitative foundation for pricing, reserving, capital allocation, and risk management. While the main body of an actuarial report presents the core findings, the appendices play a critical supporting role. They supply the data, assumptions, computational details, and supplementary results that allow reviewerssenior actuaries, auditors, regulators, and senior managementto validate the analysis and to reproduce it if required.

This page outlines the essential components that should be included in the appendices of a generalinsurance actuarial study. The guidance is applicable to a wide range of products (property, casualty, motor, liability, etc.) and to both traditional and emerging actuarial techniques.

Purpose of Appendices

Appendices serve three primary purposes:

• Transparency: They disclose the raw data and the logical steps taken, enabling independent verification.
• Reproducibility: By providing code snippets, model settings, and detailed calculations, a future analyst can repeat the study with minimal effort.
• Regulatory compliance: Many regulators require specific documentation; appendices are the vehicle for meeting those expectations.

Common Appendices in a GeneralInsurance Actuarial Report

The following list represents the most frequently encountered appendices. Not all studies will need every item, but the omission of a relevant appendix should be justified in the main body.

1. Data Sources and Data Quality
2. Methodology & Model Structure
3. Assumptions and Parameter Estimates
4. Detailed Results (tables, charts, distributions)
5. Sensitivity and Scenario Analyses
6. Regulatory & Compliance Checks
7. Glossary of Terms and Acronyms
8. References and Bibliography
9. Appendix Code (R, Python, SAS, Excel VBA)

Data Sources and Data Quality

3.1. Data Inventory

List all data files, tables, and extracts used in the analysis. Include:

• File name and version
• Date of extraction
• Source system (policy administration, claims, underwriting, external rating agencies)
• Scope (geography, line of business, policy period)

3.2. Data Validation Summary

Provide a summary of validation checks performed:

3.3. Data Transformation Log

Outline each transformation step (e.g., aggregation, censoring, inflation adjustment) with the rationale and the exact formula used.

Methodology Details

4.1. Model Framework

Describe the statistical or deterministic model employed (e.g., GLM, Bayesian hierarchical model, frequencyseverity approach, stochastic reserving model). Include a diagram if helpful.

4.2. Computational Tools

Identify software, packages, and versions (e.g., R4.3.1 with actuar and mgcv, Python3.11 with pandas, scikitlearn). Provide a brief description of the computing environment (OS, hardware).

4.3. Estimation Procedure

Detail the estimation technique (maximum likelihood, Bayesian MCMC, bootstrap). Mention convergence criteria and any diagnostics performed (e.g., residual plots, deviance, trace plots).

Model Assumptions and Parameter Estimates

5.1. Core Assumptions

• Independence of claim counts across policy years.
• Lognormal distribution for claim severity after inflation adjustment.
• Constant marginal cost of capital at 8% per annum.
• Exposure base measured in earned premiums.

5.2. Parameter Estimates

Present the calibrated parameters in a table. Include standard errors and confidence intervals where applicable.

5.3. Assumption Rationale

Explain why each assumption is reasonable for the portfolio under review. Cite empirical evidence or industry studies where possible.

Results, Tables & Charts

6.1. Summary of Expected Losses

Provide a breakdown of expected losses by line of business, region, and policy year.

6.2. Distribution Plots

Insert illustrative histograms or density plots for frequency and severity. (In a live site, these would be generated from the analysis code.)

6.3. Reserving Projections

Show the cumulative development factors and the resulting IBNR reserves for the last three accident years.

Sensitivity & Scenario Analysis

7.1. Key Sensitivity Variables

• Inflation rate (2%)
• Loss development factor (10%)
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