Generating fraud detection rules using a trained model

A computerized method comprising:

• obtaining training transaction data;
• assembling a feature vector using the training transaction data;
• generating a decision tree model using the assembled feature vector, the decision tree model including a plurality of nodes;
• training the generated decision tree model by providing at least a portion of the training transaction data as input to the decision tree model, obtaining classification output from the decision tree model, evaluating the classification output for accuracy relative to a fraud indicator, and adjusting at least one of weight factors, decision thresholds of nodes of the decision tree model, or tree structure based on the evaluated accuracy;
• determining that an impurity ratio of at least one node of the trained decision tree model exceeds an impurity ratio threshold;
• based on the determination, eliminating overfitting by pruning one or more child nodes of the at least one node to create a pruned decision tree model;
• generating a fraud detection rule using the pruned decision tree model by backtracking a path through the pruned decision tree model starting with a leaf node or pruned node and tracing the path to a root node, and, at each node along the path, generating at least a portion of the fraud detection rule based on a decision threshold of the node;
• applying the generated fraud detection rule to input transaction data;
• identifying a transaction associated with fraud in the input transaction data based on applying the generated fraud detection rule;
• and
• automatically performing a fraud mitigation action in response to identifying the transaction associated with fraud.

A computer storage medium has computer-executable instructions that, upon execution by a processor, cause the processor to at least:

• obtain training transaction data;
• assemble a feature vector using the training transaction data;
• generate a decision tree model using the assembled feature vector, the decision tree model including a plurality of nodes;
• train the generated decision tree model by providing at least a portion of the training transaction data as input to the decision tree model, obtaining classification output from the decision tree model, evaluating the classification output for accuracy relative to a fraud indicator, and adjusting at least one of weight factors, decision thresholds of nodes of the decision tree model, or tree structure based on the evaluated accuracy;
• determine that an impurity ratio of at least one node of the trained decision tree model exceeds an impurity ratio threshold;
• based on the determination, eliminate overfitting by pruning one or more child nodes of the at least one node to create a pruned decision tree model;
• generate a fraud detection rule using the pruned decision tree model by backtracking a path through the pruned decision tree model starting with a leaf node or pruned node and tracing the path to a root node, and, at each node along the path, generating at least a portion of the fraud detection rule based on a decision threshold of the node;
• apply the generated fraud detection rule to input transaction data;
• identify a transaction associated with fraud in the input transaction data based on applying the generated fraud detection rule;
• and
• automatically perform a fraud mitigation action in response to identifying the transaction associated with fraud.