US 2018/0025273 A1 · OPTIMIZING NEURAL NETWORKS FOR GENERATING ANALYTICAL OR PREDICTIVE OUTPUTS

Abstract

Certain embodiments involve generating or optimizing a neural network for generating analytical or predictive outputs. The neural network can be generated using a relationship between various predictor variables and an outcome (e.g., a condition's presence or absence). The neural network can be used to determine a relationship between each of the predictor variables and a response variable. The neural network can be optimized by iteratively adjusting the neural network such that a monotonic relationship exists between each of the predictor variables and the response variable. The optimized neural network can be used both for accurately determining response variables using predictor variables and determining adverse action codes for the predictor variables, which indicate an effect or an amount of impact that a given predictor variable has on the response variable. The neural network can be used to generate adverse action codes upon which consumer behavior can be modified to improve the response variable score.

Claims20

Dependent claims

A system comprising:

a processing device;
and
a memory device in which instructions executable by the processing device are stored for causing the processing device to:
receive a plurality of predictor variables, wherein each predictor variable corresponds to an entity;
determine a correlation between each predictor variable and an outcome;
generate a neural network that includes a hidden layer for determining a relationship between each predictor variable and a response variable based on the correlation, wherein the response variable indicates a behavior associated with the entity and wherein the neural network is operable for determining whether a monotonic relationship exists between each predictor variable and the response variable;
and
iteratively adjust the neural network so that the monotonic relationship exists between each predictor variable and the response variable as determined by the neural network.

5 more elements

A method comprising:

receiving, by a processor, a plurality of predictor variables, wherein each predictor variable corresponds to an entity;
determining, by the processor, a correlation between each predictor variable and an amount of positive outcomes or negative outcomes, wherein each positive outcome indicates that a condition is satisfied and each negative outcome indicates failure to satisfy the condition;
generating, by the processor, a neural network that includes a hidden layer for determining a relationship between each predictor variable and a response variable based on the correlation, wherein the response variable is indicates a behavior associated with the entity;
and
iteratively adjusting the neural network so that a monotonic relationship exists between each predictor variable and the response variable as determined by the neural network.

2 more elements

A non-transitory computer-readable storage medium having program code that is executable by a processor device to cause a computing device to perform operation, the operations comprising:

receiving a plurality of predictor variables, wherein each predictor variable corresponds to an entity;
determining a correlation between each predictor variable and an amount of positive outcomes or negative outcomes, wherein each positive outcome indicates that a condition is satisfied and each negative outcome indicates failure to satisfy the condition;
generating a neural network that includes a hidden layer for determining a relationship between each predictor variable and a response variable based on the correlation, wherein the response variable indicates a behavior associated with the entity and wherein the neural network is operable for determining whether a monotonic relationship exists between each predictor variable and the response variable;
and
iteratively adjusting the neural network so that the monotonic relationship exists between each predictor variable and the response variable as determined by the neural network.

2 more elements

Dependencies are inferred from the claim text.

Optimizing neural networks for generating analytical or predictive outputs

Life of this patent

Optimizing neural networks for generating analytical or predictive outputs

Life of this patent

Abstract

Claims20