Abstract: A computerized method trains an entry classifier model and uses the entry classifier model to identify multi-party anonymization transaction (MAT) entries. Labeled training data is obtained from a training data source and standard data features are identified therein. Engineered data features are generated using the identified standard data features. Training data features are selected from the standard data features and the engineered data features. A balanced training data subset is generated using the obtained labeled training data and an entry classifier model is trained to classify data entries as being in a MAT class based on the selected training data features using the balanced training data subset. The trained entry classifier model is used to classify an input data entry as being in the MAT class.

Abstract: A computer implemented method to obtain a batch transaction detection model that uses a machine learning process to detect that a transaction of a digital currency is a batch transaction is described. The method comprises obtaining transaction data from a block in a blockchain, wherein the transaction data comprises a plurality of items; generating an aggregated transaction data set of the transaction data and labelling the aggregated transaction data set according to whether the transaction is a batch transaction, using a feature selection method to remove from the aggregated transaction data redundant features and collinear features, to generate a reduced transaction data set having substantially independent features relevant to batch transaction detection. The method trains, tests and validates the batch transaction detection model using the reduced transaction data set; where the trained batch transaction detection model is adapted to determine whether a transaction is a batch transaction.

Abstract: A computer implemented method of decomposing a blockchain comprising transactions in digital currency for analysis and display is described. The method comprises first determining a range of blocks in the blockchain. Each block in the range of blocks in the blockchain is then unpacked into a table comprising one or more rows of input and output data for each transaction stored in the block. For the range of blocks in the blockchain, entity information and transaction information are then aggregated into a block analysis table. A node of a computing network and a computer program product adapted for implementation of such a method are also described.

Abstract: A computer implemented method of training a model, using a machine learning process, to predict whether a transaction of a digital currency stored in a blockchain is fraudulent, comprises: obtaining (202) transaction data for a first transaction of first funds in the digital currency, wherein the transaction data further comprises information related to a second transaction of the first funds that preceded the first transaction. The method further comprises labelling (204) the transaction data for the first transaction according to whether the first transaction was fraudulent and using (206) the transaction data and the label as training data with which to train the model.

Abstract: A computer implemented method of training a model, using a machine learning process, to predict whether a transaction of a digital currency stored in a blockchain is fraudulent, comprises: unpacking (202) a block in the blockchain into a table comprising one or more rows of input and output data for a previous transaction stored in the block and aggregating (204) the one or more rows of input and output data to form an aggregated row of transaction data for the previous transaction. The method further comprises labelling (206) the aggregated row of transaction data for the previous transaction according to whether the previous transaction was fraudulent and using (208) the aggregated row of transaction data and the label as training data with which to train the model.

Abstract: An apparatus for determining a level of risk that a future transfer will exceed a level of reserve is provided by the present disclosure, the apparatus comprising circuitry configured to: obtain data of transfers between financial institutions which have occurred at a number of instances of time, the data including transfer amounts; apply a predictive model to the data to obtain a prediction of the transfer amount at each instance of time; determine a maximum residual between the prediction of the transfer amount and the transfer amount at each instance of time; model the maximum residual for each instance of time using a generalised extreme value distribution to obtain a distribution function; and determine the level of risk that a future transfer between financial institutions will exceed the level of reserve based on the distribution function which has been obtained.