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1. (WO2018084867) OPTIMIZING AUTOMATED MODELING ALGORITHMS FOR RISK ASSESSMENT AND GENERATION OF EXPLANATORY DATA

Pub. No.:    WO/2018/084867    International Application No.:    PCT/US2016/060805
Publication Date: Sat May 12 01:59:59 CEST 2018 International Filing Date: Tue Nov 08 00:59:59 CET 2016
IPC: G06N 3/04
G06N 3/08
Applicants: EQUIFAX INC.
Inventors: TURNER, Matthew
MCBURNETT, Michael
ZHANG, Yafei
Title: OPTIMIZING AUTOMATED MODELING ALGORITHMS FOR RISK ASSESSMENT AND GENERATION OF EXPLANATORY DATA
Abstract:
Certain aspects involve optimizing neural networks or other models for assessing risks and generating explanatory data regarding predictor variables used in the model. In one example, a system identifies predictor variables compliant with certain monotonicity constraints. The system generates a neural network for determining a relationship between each predictor variable and a risk indicator. The system performs a factor analysis on the predictor variables to determine common factors. The system iteratively adjusts the neural network so that (i) a monotonic relationship exists between each common factor and the risk indicator and (ii) a respective variance inflation factor for each common factor is sufficiently low. Each variance inflation factor indicates multicollinearity among a subset of the predictor variables corresponding to a common factor. The adjusted neural network can be used to generate explanatory indicating relationships between (i) changes in the risk indicator and (ii) changes in at least some common factors.