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Insurance liabilities are converging with capital markets products (e.g. derivatives and securitizations), thereby increasing the demand for integrated asset and liability management strategies. This article compares the value‐added by an integrative approach‐based on scenario optimization modelling‐relative to traditional risk management methods. The authors present some examples of products offered by the insurance industry in Italy, and apply the results of the analysis to the design of competitive insurance policies.

Today consumers demand delivery of financial services anytime and anywhere, and their needs and desires are evolving rapidly. The World Wide Web provides a rich channel for distributing customized services to a range of clients. An Internet-based system developed by Prometeia S.r.l. for Italian banks-both traditional and e-banks-supports consumers and financial advisors in planning personal finances. The system provides advice on allocating personal assets to fund consumers' needs, such as paying for a house, children's education, retirement, or other projects. State-of-the-art models of financial engineering-based on scenario optimizationdevelop plans that are consistent with clients' goals, their attitudes towards risk, and the prevailing views on market performance. The system then helps clients to select off-the-shelf financial products, such as mutual funds, to create customized portfolios. Finally, it analyzes the risk of portfolios in terms that are intuitive for laypersons and monitors their performance in achieving the target goals. Four major banks use the system to support their networks of several thousand financial advisors and to reach tens of thousands of clients directly.

We develop a scenario optimization model for asset and liability management of individual investors. The individual has a given level of initial wealth and a target goal to be reached within some time horizon. The individual must determine an asset allocation strategy so that the portfolio growth rate will be sufficient to reach the target. A scenario optimization model is formulated which maximizes the upside potential of the portfolio, with limits on the downside risk. Both upside and downside are measured vis-à-vis the goal. The stochastic behavior of asset returns is captured through bootstrap simulation, and the simulation is embedded in the model to determine the optimal portfolio. Post-optimality analysis using out-of-sample scenarios measures the probability of success of a given portfolio. It also allows us to estimate the required increase in the initial endowment so that the probability of success is improved.

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