Distinctive Population Segments in Multi-mode Risk Management

Abstract

This chapter presents and illustrates a method to identify distinctive population segments in retirement-related risk management. Its focus is the conduct of multiple risk management activities, as measured by a composite indicator. A multivariate prediction model generates the conditional probability of the targeted behavioural outcome. The condition comprises a specified combination of attributes. These represent important causal factors in the outcome. Large multidimensional population segments with unusually high (or low) average probabilities are targeted when applying the method. Such population segments are usually represented by small subsamples in surveys, thus eliminating cross tabulations for estimating the required conditional probabilities. The method illustrated here is ready for application with suitable data sets anywhere in the world. Considering together the contrasting profiles of the high-performing and low-scoring key demographics, we find support for our theoretical position that both high and low potentials to achieve effective retirement-related risk management arise from networks of variables linked via causal chains in which no single variable is dominant. The data suggest that efforts to bring assistance to those who are not well positioned in risk management will be confronted with challenging heterogeneity among the relevant distinctive population segments.