AUTO-ENCODER ENHANCED SELF-DIAGNOSTIC COMPONENTS FOR MODEL MONITORING

摘要

A diagnostic system for model governance is presented. The diagnostic system includes an auto-encoder to monitor model suitability for both supervised and unsupervised models. When applied to unsupervised models, the diagnostic system can provide a reliable indication on model degradation and recommendation on model rebuild. When applied to supervised models, the diagnostic system can determine the most appropriate model for the client based on a reconstruction error of a trained auto-encoder for each associated model. An auto-encoder can determine outliers among subpopulations of consumers, as well as support model go-live inspections.

主权项

1. A system comprising:
a computer-implemented analytics module that receives transaction data of one or more customers, the analytics module storing a model of transactional behaviors and comparing the transaction data with the model of transactional behaviors to determine a likelihood of a specific transaction or behavior of each of the one or more customers, the analytics module further generating a score representing the likelihood of the specific behavior resembling historical data based on a historical learning of the model; a data extractor for extracting an original data sampling from the transaction data; and a computer-implemented auto-encoder coupled with the analytics module by a computer network that includes the data extractor, the auto-encoder receiving the original data sampling and calculating, using the model of the analytics module, one or more latent variables of the model for reconstructing the original data sampling with a reconstructed data set, the auto-encoder further calculating a reconstruction error for the model utilizing one or more latent variables, the reconstruction error representing a deviation of the reconstructed data record from the original data sampling.