Abstract: Examples relate to a method, a computer program and a system for enabling a verification of a result of a computation obtained from a third party. The method comprises providing a model on a distributed ledger, the model representing the computation used by the third party. The method comprises providing access to the model for one or more users, to enable the users to verify the result of the computation.

Abstract: Training a machine learning model includes selecting a subset of training transactions from a plurality of training transactions to be used for classifying transactions as either fraudulent or genuine, and classifying transactions as either fraudulent or genuine. Additionally, selecting the subset of training transactions includes clustering the plurality of training transactions into a plurality of clusters based on a similarity measure. Each training transaction includes an indication whether the training transaction is fraudulent or genuine. Further, selecting the subset of training transactions includes selecting a first proportion of the training transactions of a cluster of training transactions for the subset of training transactions if the cluster comprises at least one fraudulent training transaction, and selecting a second proportion of the training transactions of a cluster of training transactions for the subset of training transactions if the cluster is free of fraudulent training transactions.

Abstract: Examples relate to a method, a computer program and a system for enabling a verification of a result of a computation obtained from a third party. The method comprises providing a model on a distributed ledger, the model representing the computation used by the third party. The method comprises providing access to the model for one or more users, to enable the users to verify the result of the computation.

Abstract: The present disclosure relates to a concept of fraud handling. A data transaction request is received via a data network from at least one user account. The data transaction request is analyzed based on predefined fraud detection rules to generate a fraud score associated with the at least one user account. The at least one user account is classified as fraudulent account if the associated fraud score exceeds a predefined fraud likelihood threshold. Data transactions associated with a classified fraudulent account via the data network are pseudo-randomly permitted or blocked.

Abstract: The present disclosure relates to a concept of fraud handling. A data transaction request is received via a data network from at least one user account. The data transaction request is analyzed based on predefined fraud detection rules to generate a fraud score associated with the at least one user account. The at least one user account is classified as fraudulent account if the associated fraud score exceeds a predefined fraud likelihood threshold. Data transactions associated with a classified fraudulent account via the data network are done by purposely deteriorating the data transactions associated with the classified fraudulent account in comparison to data transactions associated with a classified non-fraudulent account.

Abstract: Embodiments of the present disclosure relate to a method, an apparatus and a computer program for selecting a subset of training transactions from a plurality of training transactions for training a machine-learning model to be used for classifying trans-actions as either fraudulent or genuine, and to a method for classifying transactions as either fraudulent or genuine. The method for selecting a subset of training transactions from a plurality of training transactions for training a machine-learning model to be used for classifying trans-actions as either fraudulent or genuine comprises clustering the plurality of training transactions into a plurality of clusters based on a similarity measure. Each training transaction comprises an indication whether the training transaction is fraudulent or genuine.

Abstract: A method comprising partitioning a space of user requests into subsets, and determining a risk score for a user request based on the evolution of the number of user requests in the subset of user requests to which this user request belongs.

Abstract: The present disclosure relates to a concept of fraud handling. A data transaction request is received via a data network from at least one user account. The data transaction request is analyzed based on predefined fraud detection rules to generate a fraud score associated with the at least one user account. The at least one user account is classified as fraudulent account if the associated fraud score exceeds a predefined fraud likelihood threshold. Data transactions associated with a classified fraudulent account via the data network are pseudo-randomly permitted or blocked.

Abstract: The present disclosure relates to a concept of fraud handling. A data transaction request is received via a data network from at least one user account. The data transaction request is analyzed based on predefined fraud detection rules to generate a fraud score associated with the at least one user account. The at least one user account is classified as fraudulent account if the associated fraud score exceeds a predefined fraud likelihood threshold. Data transactions associated with a classified fraudulent account via the data network are done by purposely deteriorating the data transactions associated with the classified fraudulent account in comparison to data transactions associated with a classified non-fraudulent account.

Abstract: A method comprising partitioning a space of user requests into subsets, and determining a risk score for a user request based on the evolution of the number of user requests in the subset of user requests to which this user request belongs.