This paper is concerned with learning decision makers’ preferences using data on observed choices from a ﬁnite set of risky alternatives. We propose a discrete choice model with unobserved heterogeneity in consideration sets and in standard risk aversion. We obtain suﬃcient conditions for the model’s semi-nonparametric point identiﬁcation, including in cases where consideration depends on preferences and on some of the exogenous variables. Our method yields an estimator that is easy to compute and is applicable in markets with large choice sets. We illustrate its properties using a dataset on property insurance purchases.