New regulations and stronger competition have increased the demand for advanced asset and liability management (ALM) models within insurance industry. An efficient ALM strategy can considerably impact the solvency of an insurance undertaking, which in turn affects capital requirements. Furthermore, ALM is often used as a strategic decision-making tools enabling the company to implement investment strategies as well as to achieve financial objectives beyond risk mitigation and regulatory aspects.
Successful construction of an ALM framework involves forecasting a balance sheet in order to analyse how it is affected by external and internal risk factors. A natural way to do this is to examine possible future scenarios and to estimate how the balance sheet evolves as the underlying risk factors change. In mathematics and quantitative finance this constitutes a broad class of estimation methods, collectively known as Monte Carlo. One computationally efficient variety of Monte Carlo is the least-squares Monte Carlo (LSMC) method. LSMC enables fast estimations while maintaining high accuracy and therefore serves as an adequate addition to the ALM toolbox.