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Effect of Model Assumption Interactions on Insurance Liability Estimates

Abstract

Model assumption interactions refer to the combined effects that occur when two or more actuarial assumptions influence an insurance model simultaneously. Insurance liability estimates commonly depend on assumptions relating to mortality, interest rates, expenses, claims experience, inflation, policyholder behaviour, and other risk factors. Changes in one assumption may therefore affect the impact of another assumption, making the interaction between assumptions an important consideration in actuarial liability valuation. This study will examine the effect of model assumption interactions on insurance liability estimates. It will assess how combinations of different actuarial assumptions influence estimated insurance liabilities and determine the extent to which interacting assumptions produce differences in liability values compared with changes considered independently. The study will also examine the sensitivity of insurance liability estimates to alternative combinations of key model assumptions. The study will focus on model assumption interactions, insurance liability estimates, actuarial valuation, mortality assumptions, interest rate assumptions, expense assumptions, inflation assumptions, claims experience, policyholder behaviour, and liability modelling. Selected actuarial assumptions will be varied individually and jointly to examine their effects on projected insurance liabilities. Actuarial and statistical techniques will be applied to identify the assumptions and combinations that produce substantial changes in liability estimates. A quantitative research approach will be adopted for the study. Historical insurance policy, claims, demographic, financial, and expense data will be analysed using descriptive statistics, actuarial valuation models, scenario analysis, interaction analysis, comparative analysis, and sensitivity analysis. Alternative combinations of model assumptions will be applied to the same insurance portfolio, and the resulting liability estimates will be compared to determine the effect of assumption interactions. The study is expected to reveal that interactions among model assumptions may have a significant effect on insurance liability estimates. Combined changes in assumptions may produce liability variations that differ from the effects of changing individual assumptions separately. The magnitude of these effects may depend on the type of insurance product, policy duration, portfolio characteristics, claims experience, assumption levels, and the degree of dependence between the assumptions incorporated into the valuation model. The study will be useful to actuaries, insurance companies, valuation analysts, financial managers, regulators, underwriters, risk managers, and researchers. It may provide useful information for improving actuarial assumption setting, strengthening liability valuation, identifying important assumption combinations, and supporting more comprehensive sensitivity analysis. The findings may also assist insurers in understanding how interacting assumptions can influence financial projections and insurance liability management. The study concludes that model assumption interactions are important considerations in insurance liability estimation because simultaneous changes in actuarial assumptions can materially influence projected liabilities. It is therefore recommended that insurers and actuaries evaluate key assumptions both individually and jointly, conduct appropriate interaction and sensitivity analyses, and regularly review assumption combinations to support reliable and consistent insurance liability estimates.

Keywords: Model assumption interactions, insurance liability estimates, actuarial valuation, actuarial assumptions, mortality assumptions, interest rate assumptions, expense assumptions, inflation assumptions, claims experience, liability modelling, sensitivity analysis, scenario analysis, insurance liabilities, actuarial risk, insurance valuation.