  In many countries financial service providers have to elicit their customers
risk preferences, when offering products and services. For instance, in the
Netherlands pension funds will be legally obliged to factor in their clients
risk preferences when devising their investment strategies. Therefore,
assessing and measuring the risk preferences of individuals is critical for the
analysis of individuals' behavior and policy prescriptions. In the psychology
and economics, a number of methods to elicit risk preferences have been
developed using hypothetical scenarios and economic experiments. These methods
of eliciting individual risk preferences are usually applied to small samples
because they are expensive and the implementation can be complex and not
suitable when large cohorts need to be measured. A large number of supervised
learning models ranging from linear regression to support vector machines are
used to predict risk preference measures using socio-economic register data
such as age, gender, migration background and other demographic variables in
combination with data on income, wealth, pension fund contributions, and other
financial data. The employed machine learning models cover a range of
assumptions and properties as well as a diverse set of regression metrics. The
optimum model is selected using the metrics and interpretability of the model.
The optimal models are lasso regression and gradient boosting machines with
mean average percentage error of about 30%. This is important as it helps to
estimate risk attitudes without actually measuring them. It should be noted
that with the current accuracy the tested models are not ready for deployment
for applications that require high accuracy. However, the results do indicate
which models should be used in situations that do not require the most accurate
predictions such as augmentation data for pensions' recommendation.
