Abstract: A multi-lender architecture evaluates applicant data against lending rule sets of multiple lenders. Rule sets provided by different lenders may be expressed differently. The multi-lender architecture transforms the rules in each rule set and represents the rules in a normalization grid. The normalization grid allows a single algorithm to evaluate the rules of different lenders.

Abstract: Some embodiments of the present application include obtaining first data from a data feed to be provided to a plurality of machine learning models and detecting a changepoint in the first data. In response to the changepoint being detected, a first machine learning model may be executed on the first data to obtain first output datasets. A first performance score for the first machine learning model may be computed based on the first output datasets. A second machine learning model may be caused to execute on the first data based on the first performance score satisfying a first condition.

Abstract: Some embodiments of the present application include obtaining first data from a data feed to be provided to a plurality of machine learning models and detecting a changepoint in the first data. In response to the changepoint being detected, a first machine learning model may be executed on the first data to obtain first output datasets. A first performance score for the first machine learning model may be computed based on the first output datasets. A second machine learning model may be caused to execute on the first data based on the first performance score satisfying a first condition.

Abstract: Some embodiments of the present application include obtaining first data from a data feed to be provided to a plurality of machine learning models and detecting a changepoint in the first data. In response to the changepoint being detected, a first machine learning model may be executed on the first data to obtain first output datasets. A first performance score for the first machine learning model may be computed based on the first output datasets. A second machine learning model may be caused to execute on the first data based on the first performance score satisfying a first condition.

Abstract: A multi-lender architecture evaluates applicant data against lending rule sets of multiple lenders. Rule sets provided by different lenders may be expressed differently. The multi-lender architecture transforms the rules in each rule set and represents the rules in a normalization grid. The normalization grid allows a single algorithm to evaluate the rules of different lenders.

Abstract: A multi-lender architecture evaluates applicant data against lending rule sets of multiple lenders. Rule sets provided by different lenders may be expressed differently. The multi-lender architecture transforms the rules in each rule set and represents the rules in a normalization grid. The normalization grid allows a single algorithm to evaluate the rules of different lenders.

Abstract: A multi-lender architecture evaluates applicant data against lending rule sets of multiple lenders. Rule sets provided by different lenders may be expressed differently. The multi-lender architecture transforms the rules in each rule set and represents the rules in a normalization grid. The normalization grid allows a single algorithm to evaluate the rules of different lenders.