# US 2018/0025273 A1 · Optimizing neural networks for generating analytical or predictive outputs > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2018/0025273 A1 - Document ID: US20180025273A1 - Status: Published application - Kind: A1 - Application number: 15/724,828 - Filed: 2017-10-04 - Published: 2018-01-25 - Earliest claimed: 2015-03-27 - Assignees: Equifax Inc. - Applicants: Equifax Inc. - Inventors: Lewis Jordan; Matthew Turner; Michael McBurnett - Web page: https://patentgrep.com/patent/US20180025273A1 ## CPC - G06N 3/084: Backpropagation, e.g. using gradient descent - G06F 17/18: for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)} - G06N 3/0499: Feedforward networks - G06N 3/082: modifying the architecture, e.g. adding, deleting or silencing nodes or connections - G06N 3/09: Supervised learning - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - G06N 20/00: Machine learning - G06Q 40/00: Finance; Insurance; Tax strategies; Processing of corporate or income taxes - G06Q 40/03: Credit; Loans; Processing thereof - G06Q 40/08: Insurance - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR ## 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. ## Claims ### Claim 1 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. ### Claim 2 (depends on 1) The system of claim 1, wherein the processing device is configured to: adjust the neural network by adjusting at least one of a number of nodes in the hidden layer of the neural network, a predictor variable in the plurality of predictor variables, or a number of layers in the hidden neural network; determine, using the neural network, the response variable based at least partially on the predictor variables after the monotonic relationship exists between each predictor variable and the response variable; determine, based on a rate of change of the response variable with respect to the predictor variable, that the monotonic relationship exists between each predictor variable and the response variable; determine, using the neural network, an impact of each predictor variable on the response variable; and generate, using the neural network, an adverse action code associated with each predictor variable that indicates the impact of each predictor variable on the response variable. ### Claim 3 (depends on 2) The system of claim 2, wherein the hidden layer comprises at least two hidden layers. ### Claim 4 (depends on 1) The system of claim 1, wherein the processing device is configured to determine the correlation between each predictor variable and an outcome by 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 ### Claim 5 (depends on 4) The system of claim 4, wherein the processing device is configured to determine the correlation between each predictor variable and the amount of positive outcomes or negative outcomes by performing operations comprising verifying that a bivariate relationship exists between each predictor variable and the amount of positive or negative outcomes. ### Claim 6 (depends on 2) The system of claim 2, wherein the processing device is further configured to determine a rank of each predictor variable, using the neural network, based on the impact of each predictor variable on the response variable. ### Claim 7 (depends on 1) The system of claim 1, wherein the response variable corresponds to a credit score of the entity. ### Claim 8 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. ### Claim 9 (depends on 8) The method of claim 8, wherein iteratively adjusting the neural network includes determining whether the monotonic relationship exists between each predictor variable and the response variable. ### Claim 10 (depends on 8) The method of claim 8, wherein adjusting the neural network includes adjusting at least one of a number of nodes in the hidden layer of the neural network, a predictor variable in the plurality of predictor variables, or a number of layers in the hidden neural network, wherein the method further comprises: determining, using the neural network, the response variable based at least partially on the predictor variables after the monotonic relationship exists between each predictor variable and the response variable; determining, based on a rate of change of the response variable with respect to the predictor variable, that the monotonic relationship exists between each predictor variable and the response variable; determining, using the neural network, an impact of each predictor variable on the response variable; determining, using the neural network, an adverse action code associated with each predictor variable that indicates the impact of each predictor variable on the response variable; and determining, using the neural network, a rank of each predictor variable based on the impact of each predictor variable on the response variable. ### Claim 11 (depends on 10) The method of claim 10, further comprising outputting, by the processor, the response variable, each predictor variable, the adverse action code associated with each predictor variable, and the rank of each predictor variable. ### Claim 12 (depends on 10) The method of claim 10, wherein the hidden layer comprises at least two hidden layers. ### Claim 13 (depends on 8) The method of claim 8, wherein determining the correlation between each predictor variable and the amount of positive outcomes or negative outcomes includes verifying that a bivariate relationship exists between each predictor variable and the amount of positive or negative outcomes. ### Claim 14 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. ### Claim 15 (depends on 14) The non-transitory computer-readable medium of claim 14, wherein the operations further comprise: adjusting the neural network by adjusting at least one of a number of nodes in the hidden layer of the neural network, a predictor variable in the plurality of predictor variables, or a number of layers in the hidden neural network; determining, using the neural network, the response variable based at least partially on the predictor variables after the monotonic relationship exists between each predictor variable and the response variable; determining, based on a rate of change of the response variable with respect to the predictor variable, that the monotonic relationship exists between each predictor variable and the response variable; determining, using the neural network, an impact of each predictor variable on the response variable; and generating, using the neural network, an adverse action code associated with each predictor variable that indicates the impact of each predictor variable on the response variable. ### Claim 16 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the operations further comprise determining, using the neural network, a rank of each predictor variable based on the impact of each predictor variable on the response variable. ### Claim 17 (depends on 16) The non-transitory computer-readable medium of claim 16, wherein the operations further comprise selecting, using the neural network, a subset of the predictor variables based on the ranks of the selected predictor variables. ### Claim 18 (depends on 17) The non-transitory computer-readable medium of claim 17, wherein the operations further comprise outputting, by the processor, the response variable, each predictor variable, the adverse action code associated with each predictor variable, and the rank of each predictor variable ### Claim 19 (depends on 16) The non-transitory computer-readable medium of claim 16, wherein the hidden layer comprises at least two hidden layers. ### Claim 20 (depends on 14) The non-transitory computer-readable medium of claim 14, wherein the operations further comprise determining the correlation between each predictor variable and the amount of positive outcomes or negative outcomes by verifying that a bivariate relationship exists between each predictor variable and the amount of positive or negative outcomes.