Techniques are disclosed relating to classifying transactions using post-transaction information. Training architecture may be used to train a first classifier module using first data for a set of transactions as training data input, where the first data includes both pre-transaction information and post-transaction information for transactions in the set of transactions. During training of the first classifier module, in disclosed techniques, correct classifications for the transaction in the set of transactions are known. The training architecture, in disclosed techniques, generates respective weights for multiple transactions in the set of transactions based on classification outputs of the trained first classifier for the multiple transactions. In disclosed techniques, the training architecture trains a second classifier module, based on the generated weights, using second data for the set of transactions as training data input. In some embodiments, the trained second classifier module classifies one or more pending transactions based only on pre-transaction information for the one or more pending transactions.