Abstract: Disclosed are systems and methods for consolidating credit profiles for co-applicants of a joint loan application. For example, a method may include: obtaining a first model score associated with the first applicant; receiving an address of the first applicant; obtaining a second model score associated with the second applicant; receiving an address of the second applicant; determining that the address of the first applicant and the address of the second applicant are the same; aggregating the first model score and the second model score to generate a joint model score for the first and second applicants based on the determination that the address of the first applicant and the address of the second applicant are the same; and generating the joint application for the first and second applicants at least based on the joint model score.

Abstract: Methods and systems are disclosed for determining a performance shift due to an applied condition, excluding other, e.g., environmental, factors. One mechanism for determining a performance shift due to an applied condition involves using two datasets (e.g., a control dataset and a test dataset) for two different populations. A training dataset is used to train a machine learning model to build a control dataset to be used in a second machine learning model together with a test dataset to determine whether a performance shift between the two datasets is due to the condition that was applied to the test dataset.

Abstract: Methods and systems are described herein for a mechanism for detecting model bias using a test model to compare predictions for specific categories of data. Thus, the system may detect category-specific model bias and/or present alternative predictions, for example, via overfitted and alternative machine learning models. Another mechanism for detecting model bias is to use residual data from predictions obtained from a machine learning model as the machine learning model processes different datasets (e.g., a training dataset and a compare dataset).

Abstract: Methods and systems are described herein for a mechanism for detecting model bias using a test model to compare predictions for specific categories of data. Thus, the system may detect category-specific model bias and/or present alternative predictions, for example, via overfitted and alternative machine learning models. Another mechanism for detecting model bias is to use residual data from predictions obtained from a machine learning model as the machine learning model processes different datasets (e.g., a training dataset and a compare dataset).

Abstract: Disclosed are systems and methods for consolidating credit profiles for co-applicants of a joint loan application. For example, a method may include: obtaining a first model score associated with the first applicant; receiving an address of the first applicant; obtaining a second model score associated with the second applicant; receiving an address of the second applicant; determining that the address of the first applicant and the address of the second applicant are the same; aggregating the first model score and the second model score to generate a joint model score for the first and second applicants based on the determination that the address of the first applicant and the address of the second applicant are the same; and generating the joint application for the first and second applicants at least based on the joint model score.

Abstract: Systems, methods, and computer readable media are disclosed for generating, modifying, and using machine learning models to predict and evaluate differences between groups. Methods disclosed herein may include identifying variables that characterize members of a first group, generating shift indicators using the identified variables, generating a machine learning model using the shift indicators and the first group, using the machine learning model and the group to predict shifts between the first group and a predicted second group, determining an aggregate population shift and an aggregate performance shift between the first group and an actual second group, and identifying an impact of one or more of the shift indicators on the aggregate population shift or performance shift. Systems and methods disclosed herein may be configured to receive requests to predict and evaluate differences between group, and to return such predictions and evaluations to one or more users.

Abstract: Systems, methods, and computer readable media are disclosed for generating, modifying, and using machine learning models to predict and evaluate differences between groups. Methods disclosed herein may include identifying variables that characterize members of a first group, generating shift indicators using the identified variables, generating a machine learning model using the shift indicators and the first group, using the machine learning model and the group to predict shifts between the first group and a predicted second group, determining an aggregate population shift and an aggregate performance shift between the first group and an actual second group, and identifying an impact of one or more of the shift indicators on the aggregate population shift or performance shift. Systems and methods disclosed herein may be configured to receive requests to predict and evaluate differences between group, and to return such predictions and evaluations to one or more users.

Abstract: Systems, methods, and computer readable media are disclosed for generating, modifying, and using machine learning models to predict and evaluate differences between groups. Methods disclosed herein may include identifying variables that characterize members of a first group, generating shift indicators using the identified variables, generating a machine learning model using the shift indicators and the first group, using the machine learning model and the group to predict shifts between the first group and a predicted second group, determining an aggregate population shift and an aggregate performance shift between the first group and an actual second group, and identifying an impact of one or more of the shift indicators on the aggregate population shift or performance shift. Systems and methods disclosed herein may be configured to receive requests to predict and evaluate differences between group, and to return such predictions and evaluations to one or more users.