Abstract: An online system uses a trained machine-learning model for dynamically modifying an authorization buffer amount to cover additional expenses occurring during fulfillment of an online order. Upon receiving a signal indicating that a user entered an online checkout stage of the order, the online system applies the machine-learning model to generate a set of values of a metric for a set of authorization buffer amounts, each value of the metric resulting from charging the user a respective authorization buffer amount over an expected value of the order if a value of the order at delivery is greater than the expected value. The online system selects an authorization buffer amount resulting in the largest value of the metric, and generates an authorization signal that authorizes charging the user the authorization buffer amount over the expected value if the value of the order is greater than the expected value.