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In many countries financial service providers have to elicit their customersrisk preferences, when offering products and services. For instance, in theNetherlands pension funds will be legally obliged to factor in their clientsrisk preferences when devising their investment strategies. Therefore,assessing and measuring the risk preferences of individuals is critical for theanalysis of individuals' behavior and policy prescriptions. In the psychologyand economics, a number of methods to elicit risk preferences have beendeveloped using hypothetical scenarios and economic experiments. These methodsof eliciting individual risk preferences are usually applied to small samplesbecause they are expensive and the implementation can be complex and notsuitable when large cohorts need to be measured. A large number of supervisedlearning models ranging from linear regression to support vector machines areused to predict risk preference measures using socio-economic register datasuch as age, gender, migration background and other demographic variables incombination with data on income, wealth, pension fund contributions, and otherfinancial data. The employed machine learning models cover a range ofassumptions and properties as well as a diverse set of regression metrics. Theoptimum model is selected using the metrics and interpretability of the model.The optimal models are lasso regression and gradient boosting machines withmean average percentage error of about 30%. This is important as it helps toestimate risk attitudes without actually measuring them. It should be notedthat with the current accuracy the tested models are not ready for deploymentfor applications that require high accuracy. However, the results do indicatewhich models should be used in situations that do not require the most accuratepredictions such as augmentation data for pensions' recommendation.

Models towards Risk Behavior Prediction and Analysis: A Netherlands Case study