Predicting Fraudulent Transactions

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.

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