Zestfinance, Inc.

John Candido

Douglas C Merrill

Jerome Louis Budzik

Sean Javad Kamkar

Derek Wilcox

Michael Edward Ruberry

Esfandiar Alizadeh

Benjamin Anthony Solecki

Jiahuan He

Yachen Yan

Ozan Sayin

Vishwaesh Rajiv

Armen Avedis Donigian

Evan George Kriminger

John Wickens Lamb Merrill

John Joseph Beahan, Jr

Feng Li

Bojan Tunguz

Melanie Eunique DeBruin

Randolph Paul Sinnott, Jr

John W L Merrill

Liubo Li

Carlos Alberta Huertas Villegas

Eran Dvir

Lin Song

Shawn M Budde

Lingyun Gu

James P McGuire

Farshad Kheiri

Manoj Pinnamaneni

Marick Sinay

This invention relates generally to the personal finance and banking field, and more particularly to the field of lending and credit notification methods and systems. Preferred embodiments of the present invention provide systems and methods for automatically generating high quality adverse action notifications based on identifying variations between a declined borrower's profile and that of approved applicants, both with simple and sophisticated credit scoring systems, using specific algorithms.

Adverse action systems and methods for communicating adverse action notifications for processing systems using different ensemble modules

Systems and methods for generating and processing modeling workflows are disclosed. In some examples, a reference distribution of scores generated by a model is determined. The reference distribution of scores is recorded in a structured database. One or more unexpected scores are detected during execution of the model. To detect the one or more unexpected scores, a production distribution of scores is compared with the reference distribution of scores recorded in the structured database. The production distribution of scores is generated by the model for a production input data set. An alert is then provided to an external system, when an alert condition is determined to be satisfied based on the comparison. The alert indicates detection of the one or more unexpected scores.

Systems and methods for enriching modeling tools and infrastructure with semantics

Systems and methods for model evaluation. A model is evaluated by performing a decomposition process for a model output, relative to a baseline input data set.

Systems and methods for providing machine learning model evaluation by using decomposition

Systems and methods for model evaluation. A protected class model that satisfies an accuracy threshold is built by using: data sets for use by a modeling system being evaluated, and protected class membership information for each data set. A target for the protected class model is a protected class membership variable indicating membership in a protected class. Each predictor of the protected class model is a predictor of an evaluated model used by the modeling system. A target of the evaluated model is different from the target of the protected class model. Each predictor is a set of one or more variables of the data sets. For each predictor of the protected class model, a protected class model impact ranking value and a modeling system impact ranking value are determined.

Explainable machine learning financial credit approval model for protected classes of borrowers

Systems and methods for generating and processing modeling workflows.

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Burbank, CA, United States of America

John Candido