A fraud prevention system comprising: a fraud prevention server including an electronic processor and a memory, the memory including a feature drift hardened online application origination service, wherein, when executing the feature drift hardened OAO service, the electronic processor is configured to detect, with respect to a first OAO model, feature drift in a dataset of an online application that exceeds a predefined threshold, generate one or more feature drift hardened OAO models that mitigate the feature drift, determine a fraud score of the online application based on the one or more feature drift hardened OAO models that differentiates between a behavior of a normal user and a behavior of a nefarious actor during a submission of the online application on a device and mitigates the feature drift, and control a client server to approve, hold, or deny the online application based on the fraud score that is determined; wherein the first OAO model includes a first feature set with first behavioral features, wherein the one or more feature drift hardened OAO models include a second feature set with second behavioral features, and wherein ranges associated with the second behavioral features mitigate the feature drift detected in the dataset with respect to the first OAO model. a fraud prevention server including an electronic processor and a memory, the memory including a feature drift hardened online application origination service, wherein, when executing the feature drift hardened OAO service, the electronic processor is configured to detect, with respect to a first OAO model, feature drift in a dataset of an online application that exceeds a predefined threshold, generate one or more feature drift hardened OAO models that mitigate the feature drift, determine a fraud score of the online application based on the one or more feature drift hardened OAO models that differentiates between a behavior of a normal user and a behavior of a nefarious actor during a submission of the online application on a device and mitigates the feature drift, and control a client server to approve, hold, or deny the online application based on the fraud score that is determined; wherein the first OAO model includes a first feature set with first behavioral features, wherein the one or more feature drift hardened OAO models include a second feature set with second behavioral features, and wherein ranges associated with the second behavioral features mitigate the feature drift detected in the dataset with respect to the first OAO model. detect, with respect to a first OAO model, feature drift in a dataset of an online application that exceeds a predefined threshold, generate one or more feature drift hardened OAO models that mitigate the feature drift, determine a fraud score of the online application based on the one or more feature drift hardened OAO models that differentiates between a behavior of a normal user and a behavior of a nefarious actor during a submission of the online application on a device and mitigates the feature drift, and control a client server to approve, hold, or deny the online application based on the fraud score that is determined; wherein the first OAO model includes a first feature set with first behavioral features, wherein the one or more feature drift hardened OAO models include a second feature set with second behavioral features, and wherein ranges associated with the second behavioral features mitigate the feature drift detected in the dataset with respect to the first OAO model.