System, Method, and Computer Program Product for Determining a Reason for a Deep Learning Model Output

A computing system comprising:

• one or more processors programmed and/or configured to:
• obtain training data associated with a plurality of training samples and a plurality of first labels for the plurality of training samples;
• train a first neural network by:
• providing, as input to the first neural network, the plurality of training samples,
• receiving, as output from the first neural network, a plurality of first predictions for the plurality of training samples, wherein the plurality of first predictions includes a plurality of first probabilities that a plurality of transactions associated with the plurality of training samples are fraudulent transactions, and
• modifying one or more first weights of the first neural network according to a first objective function that depends on the plurality of first labels and the plurality of first predictions to generate a first trained neural network;
• train a second neural network different than the first neural network by:
• providing as input to the second neural network, the plurality of training samples,
• receiving, as output from the second neural network, a plurality of second predictions for the plurality of training samples, wherein the plurality of second predictions includes a plurality of second probabilities that the plurality of transactions associated with the plurality of training samples are fraudulent transactions, and
• modifying one or more second weights of the second neural network according to a second objective function that depends on the plurality of first predictions as a plurality of second labels for the plurality of training samples and the plurality of second predictions to generate a second trained neural network;
• remove, from the second trained neural network, the one or more modified second weights;
• obtain transaction data including a plurality of features xi associated with a transaction currently being processed in a transaction processing network;
• process, using the trained first neural network, the transaction data including the plurality of features xi associated with the transaction to generate a prediction for the transaction;
• determine in real-time during processing of the at least one transaction in the transaction processing network, using the one or more modified second weights removed from the trained second neural network, a contribution score for each feature xi of the plurality of features xi to the at least one prediction for the transaction generated by the first neural network;
• and
• provide, with the at least one processor, to a user device, in real-time during processing of the at least one transaction in the transaction processing network, the prediction and the contribution score for each feature xi as an indication of a contribution of that feature xi to the prediction.

A computer program product comprising at least one non-transitory computer-readable medium including program instructions that, when executed by at least one processor, cause the at least one processor to:

• obtain training data associated with a plurality of training samples and a plurality of first labels for the plurality of training samples;
• train a first neural network by:
• providing, as input to the first neural network, the plurality of training samples,
• receiving, as output from the first neural network, a plurality of first predictions for the plurality of training samples, wherein the plurality of first predictions includes a plurality of first probabilities that a plurality of transactions associated with the plurality of training samples are fraudulent transactions, and
• modifying one or more first weights of the first neural network according to a first objective function that depends on the plurality of first labels and the plurality of first predictions to generate a first trained neural network;
• train a second neural network different than the first neural network by:
• providing as input to the second neural network, the plurality of training samples,
• receiving, as output from the second neural network, a plurality of second predictions for the plurality of training samples, wherein the plurality of second predictions includes a plurality of second probabilities that the plurality of transactions associated with the plurality of training samples are fraudulent transactions, and
• modifying one or more second weights of the second neural network according to a second objective function that depends on the plurality of first predictions as a plurality of second labels for the plurality of training samples and the plurality of second predictions to generate a second trained neural network,
• remove, from the second trained neural network, the one or more modified second weights;
• obtain transaction data including a plurality of features xi associated with a transaction currently being processed in a transaction processing network;
• process, using the trained first neural network, the transaction data including the plurality of features xi associated with the transaction to generate a prediction for the transaction;
• determine in real-time during processing of the at least one transaction in the transaction processing network, using the one or more modified second weights removed from the trained second neural network, a contribution score for each feature xi of the plurality of features xi to the at least one prediction for the transaction generated by the first neural network;
• and
• provide, with the at least one processor, to a user device, in real-time during processing of the at least one transaction in the transaction processing network, the prediction and the contribution score Contribution (xi->ym) for each feature xi as an indication of a contribution of that feature xi to the prediction.