Model acceptability prediction system and techniques

A system, comprising:

• a storage device;
• and
• logic, at least a portion of the logic implemented in circuitry coupled to the storage device, the logic to:
• receive a model to be reviewed, the model associated with at least one of a set of input parameters, a model type, or a data profile;
• determine a set of reviewed models comprising approved models and unapproved models, the approved models having been approved by a regulatory body, and the unapproved models having been disapproved by the regulatory body, and each of the reviewed models comprising a respective second set of input parameters, a second model type, and a second data profile;
• perform a clustering operation to cluster the set of reviewed models using the respective second sets of input parameters, the second model type and the second data profile;
• train a neural network with the clustered set of reviewed models to predict acceptability of the model;
• generate an acceptability prediction comprising a probability of acceptability for the received model by processing the received model through the neural network;
• store the acceptability prediction in a memory;
• determine whether the probability of acceptability for the received model is greater than or equal to an acceptability threshold;
• in response to the determination the probability of acceptability is greater than or equal to the acceptability threshold, include the received model as an approved model in the set of review models;
• in response to the determination the probability of acceptability is not greater than the acceptability threshold, include the received model as an unapproved model in the set of review models;
• and
• return the acceptability prediction and an indication as to whether the received model is approved or unapproved for output at a user interface.

A computer-implemented method, comprising:

• receiving a model to be reviewed, the model associated with at least one of a set of input parameters, a model type, or a data profile;
• determining a set of reviewed models comprising approved models and unapproved models, the approved models having been approved by a regulatory body, and the unapproved model having been disapproved by the regulatory body, and each of the reviewed models comprising a respective second set of input parameters, a second model type, and a second data profile;
• performing a clustering operation to cluster the set of reviewed models using the respective second sets of input parameters, the second model type and the second data profile;
• train a neural network with the clustered set of reviewed models to predict acceptability of the model;
• generating an acceptability prediction comprising a probability of acceptability for the received model by processing the received model through the neural network;
• storing the acceptability prediction in a memory;
• determining whether the probability of acceptability for the received model is greater than an acceptability threshold;
• in response to determining the probability of acceptability is greater than the acceptability threshold, include the received model as an approved model in the set of review models;
• in response to determining the probability of acceptability is not greater than the acceptability threshold, do not include the received model as an unapproved model in the set of review models;
• returning the acceptability prediction and an indication as to whether the received model is included in the set of review models or not in the set of review models for output at a user interface.