Methods and systems for automatic authorization using machine learning algorithm

A system for automatic processing prescription, comprising:

• a storage device to store a plurality of machine learning algorithms;
• a subsystem in communication with the storage device and configured to:
• optimize each of a plurality of deep learning/machine learning models by at least addressing class imbalances among a first subset of historical records;
• train each of the plurality of deep learning/machine learning models using the first subset of historical records, wherein training each of the plurality of deep learning/machine learning models comprises each of the plurality of deep learning/machine learning models respectively analyzing each of the first subset of the historical records to determine a relationship between each target column of the respective historical records in the first subset of historical records and respective predictor columns of the respective historical records in the first subset of historical records;
• implement each of the plurality of deep learning/machine learning models, as trained, to predict the value of respective known target columns in each of a second subset of historical records based on respective predictor columns in each of the second subset of historical records;
• determine respective success rates for each of the plurality of deep learning/machine learning models at predicting the respective known target columns for each of the second subset of historical records, wherein each success rate comprises an accuracy rate and a confusion metric of each of the plurality of deep learning/machine learning models at predicting the respective known target columns for each of the second subset of historical records;
• select a first of the plurality of deep learning/machine learning models having a highest success rate;
• and
• implementing the first of the plurality of deep learning/machine learning models to decide a target column of a received authorization request based on predictor columns in the received authorization request.

A non-transitory machine-readable medium comprising instructions, which, when executed by one or more processors, cause the one or more processors to perform the following operations:

• optimize each of a plurality of deep learning/machine learning models by at least addressing class imbalances among a first subset of historical records;
• train each of the plurality of deep learning/machine learning models using the first subset of historical records, wherein training each of the plurality of deep learning/machine learning models comprises each of the plurality of deep learning/machine learning models respectively analyzing each of the first subset of the historical records to determine a relationship between each target column of the respective historical records in the first subset of historical records and respective predictor columns of the respective historical records in the first subset of historical records;
• implement each of the plurality of deep learning/machine learning models, as trained, to predict the value of respective known target columns in each of a second subset of historical records based on respective predictor columns in each of the second subset of historical records;
• determine respective success rates for each of the plurality of deep learning/machine learning models at predicting the respective known target columns for each of the second subset of historical records, wherein each success rate comprises an accuracy rate and a confusion metric of each of the plurality of deep learning/machine learning models at predicting the respective known target columns for each of the second subset of historical records;
• select a first of the plurality of deep learning/machine learning models having a highest success rate;
• and
• implementing the first of the plurality of deep learning/machine learning models to decide a target column of a received authorization request based on predictor columns in the received authorization request.