Steven Morrison and his team of actuaries and quantitative analysts generate modeling techniques and tools for insurers. Based on his research and advisory work on developing modeling methodology, insurers can project their financial statements, determine risk and capital assessment, and make sound decisions. Currently, Steven’s focus on IFRS 17 specifically helps insurers understand and communicate profits.
Economic Scenarios: Moody's Analytics provides internally and globally consistent economic, regulatory, and custom scenarios.
IFRS 17 Insurance Contracts: The Moody’s Analytics suite of software solutions, models, content, and services helps support the new requirements of IFRS 17 Insurance Contracts.
Regulatory Capital : Moody’s Analytics insurance regulatory capital solutions help insurers comply with Solvency II and other similar regulatory regimes.
Econometric Modeling: Fully transparent econometric and statistical models to assess performance of geographies, financials and various asset classes.
Liability Valuation: Process of valuing a company's liabilities for financial reporting purposes.
Economic Risk Assessment: Quantitative economic assessment to help you understand the impact of forward-looking changes on the performance of your business and portfolios.
In this paper we explore the use of the carrier approximation for the multi-year projection of risk and capital metrics. Through an annuity book run-off case study we outline the key factors influencing the performance of the carrier method when applied to the projected IFRS 17 risk adjustment and the projected Solvency II solvency capital ratio (SCR).
Steven Morrison's second whitepaper, Profit Emergence under IFRS 17, turns its attention to the Variable Fee Approach (VFA). Explore his practical insights on financial risk and its impact on contracts with participation features.
An emerging business requirement for North American insurers is the ability to project forward stochastic reserve and capital requirements under various planning scenarios to a specific future date. In this paper we consider applying proxy functions to this task, using function fitting techniques described in our previous research paper Fitting Proxy Functions for Conditional Tail Expectation: Comparison of Methods.
The ability to project financial statements to understand their sensitivity to market risks, insurance risks, and methodology decisions is critical for an effective IFRS 17 implementation.
This paper details alternative methods for fitting proxy functions to CTE, employing quantile regression in combination with OLS among other techniques. We compare methods according to quality of fit for an example portfolio of variable annuities.
In this paper, we have considered the use of proxy models as a way of overcoming some of the operational and computational challenges associated with measuring future solvency under different market conditions and ALM assumptions.