Automated rule creation for fraud detection using a machine learning-driven computing framework

A method to generate machine learning (ML) rules for fraud detection based on an automated feature selection using a rule creation system, which method comprises:

• accessing a training dataset for determining a plurality of detection rules for a fraud detection ML task of an ML engine;
• performing a data preprocessing and a data sampling on the training dataset to obtain a processed and sampled dataset;
• obtaining a plurality of features from the processed and sampled dataset using a feature engineering operation;
• selecting a subset of features from the plurality of features using simulated annealing operations for the automated feature selection, wherein the simulated annealing operations select the subset of features based on an optimal combination of the plurality of features for the fraud detection ML task;
• generating the plurality of detection rules using the subset of features and at least one decision tree training ML operation;
• iteratively selecting from the plurality of detection rules for a subset of detection rules based on a calculated false positive ratio and a calculated detection rate for each of the plurality of detection rules when processing the training dataset;
• and
• evaluating a rule performance of each rule in the subset of detection rules.

A non-transitory computer-readable medium having stored thereon computer-readable instructions executable to generate machine learning (ML) rules for fraud detection based on an automated feature selection using a rule creation system, the computer-readable instructions executable to perform rule creation operations which comprise:

• accessing a training dataset for determining a plurality of detection rules for a fraud detection ML task of an ML engine;
• performing a data preprocessing and a data sampling on the training dataset to obtain a processed and sampled dataset;
• obtaining a plurality of features from the processed and sampled dataset using a feature engineering operation;
• selecting a subset of features from the plurality of features using simulated annealing operations for the automated feature selection, wherein the simulated annealing operations select the subset of features based on an optimal combination of the plurality of features for the fraud detection ML task;
• generating the plurality of detection rules using the subset of features and at least one decision tree training ML operation;
• iteratively selecting from the plurality of detection rules for a subset of detection rules based on a calculated false positive ratio and a calculated detection rate for each of the plurality of detection rules when processing the training dataset;
• and
• evaluating a rule performance of each rule in the subset of detection rules.