Abstract: A computer-implemented method for generating higher-level features based on one or more lower-level features of a data set includes generating a higher-level feature using a predefined augmentation of one or more lower-level features, wherein the predefined augmentation comprises a predefined transformation of a lower-level feature and/or a predefined combination of a plurality of lower-level features. The method further includes computing a bivariate similarity metric indicative of a similarity between the generated higher-level feature and the one or more lower-level features. Furthermore, the method comprises adding the higher-level feature to a feature graph, if the metric is less than a predefined threshold. Further, the method comprises outputting a result indicative of the feature graph comprising the lower-level features and the higher-level features.

Abstract: The disclosure relates to a method (100) for assessing user authorization, the method comprising: receiving (110), via a data communication network (330), a request from a user device (300) for an access; generating (120), based on data associated with the request, a risk score indicating a risk that the request was sent by a non-authorized user, wherein the risk score indicates a high risk, a medium risk, or a low risk that the user (400) is a non-authorized user; and signaling (130), via the data communication network (330), the user device (300) a need for further information to enable a decision about the authorization of the user (400), if the risk score indicates medium risk. A further aspect relates to a method (200) for user authorization and to an electronic device (300).

Abstract: The disclosure relates to a method (100) for assessing user authorization, the method comprising: receiving (110), via a data communication network (330), a request from a user device (300) for an access; generating (120), based on data associated with the request, a risk score indicating a risk that the request was sent by a non-authorized user, wherein the risk score indicates a high risk, a medium risk, or a low risk that the user (400) is a non-authorized user; and signaling (130), via the data communication network (330), the user device (300) a need for further information to enable a decision about the authorization of the user (400), if the risk score indicates medium risk. A further aspect relates to a method (200) for user authorization and to an electronic device (300).

Abstract: Embodiments of the present disclosure relate to a method, an apparatus and a computer program for providing a machine-learning system to be used for classifying transactions as either fraudulent or genuine, and a method, an apparatus and a computer program for classifying transactions as either fraudulent or genuine. The method for generating a machine-learning system for classifying transactions as either fraudulent or genuine based on a plurality of training transactions, each training transaction being associated with labelling information that indicates whether the training transaction is either genuine or fraudulent, comprises clustering the plurality of training transactions into a plurality of clusters based on a similarity measure. The method comprises determining, for each of the plurality of clusters, whether the cluster is homogeneous or heterogeneous. A heterogeneous cluster includes both fraudulent and genuine training transactions.