Adverse action methodology for credit risk models

A computing system comprising:

• a memory;
• a machine learning based credit risk model trained based on a set of training data;
• and
• one or more processors in communication with the memory and configured to:
• calculate, using the credit risk model, a credit risk score for one or more members of a population of consumers as output from the credit risk model based on applying values for a plurality of characteristics for each of the one or more members of the population of consumers as input to the credit risk model;
• create a top scoring credit profile of a top scoring portion of the population of consumers based on the credit risk scores for the one or more members of the population of consumers;
• receive a credit request of an applicant;
• calculate, using the credit risk model, a credit risk score for the applicant as output from the credit risk model based on applying a credit profile of the applicant as input to the credit risk model, wherein the credit profile includes applicant values for the plurality of characteristics assessed by the credit risk model;
• in response to a denial of the credit request of the applicant, determine one or more principal adverse action factors for the denial of the credit request of the applicant, wherein to determine the one or more principal adverse action factors, the one or more processors are configured to:
• determine an anchor value corresponding to each characteristic of the plurality of characteristics assessed by the credit risk model as an average value for the respective characteristic from the top scoring credit profile of the population of consumers,
• create a modified set of applicant values for each characteristic of the plurality of characteristic by replacing an applicant value for a respective characteristic with the corresponding anchor value for the respective characteristic while keeping the applicant values for the other characteristics static,
• calculate, using the credit risk model, a replacement score for each characteristic of the plurality of characteristics as output from the credit risk model based on applying the modified set of applicant values for the respective characteristic as input to the credit risk model, and
• rank each characteristic of the plurality of characteristics based on a score difference between the credit risk score for the applicant and the replacement score for the respective characteristic;
• and
• output a report identifying one or more top ranked characteristics of the plurality of characteristics as the one or more principal adverse action factors for the denial of the credit request of the applicant.

Non-transitory computer readable media comprising instructions that, when executed by one or more processors, cause the one or more processors to:

• calculate, using a machine learning based credit risk model trained based on a set of training data, a credit risk score for one or more members of a population of consumers as output from the credit risk model based on applying values for a plurality of characteristics for each of the one or more members of the population of consumers as input to the credit risk model;
• create a top scoring credit profile of a top scoring portion of the population of consumers based on the credit risk scores for the one or more members of the population of consumers;
• receive a credit request of an applicant;
• calculate, using the credit risk model, a credit risk score for the applicant as output from the credit risk model based on applying a credit profile of the applicant as input to the credit risk model, wherein the credit profile includes applicant values for the plurality of characteristics assessed by the credit risk model;
• in response to a denial of the credit request of the applicant, determine one or more principal adverse action factors for the denial of the credit request of the applicant, wherein to determine the one or more principal adverse action factors, the instruction cause the one or more processors to:
• determine an anchor value corresponding to each characteristic of the plurality of characteristics assessed by the credit risk model as an average value for the respective characteristic from the top scoring credit profile of the population of consumers,
• create a modified set of applicant values for each characteristic of the plurality of characteristics by replacing an applicant value for a respective characteristic with the corresponding anchor value for the respective characteristic while keeping the applicant values for the other characteristics static,
• calculate, using the credit risk model, a replacement score for each characteristic of the plurality of characteristics as output from the credit risk model based on applying the modified set of applicant values for the respective characteristic as input to the credit risk model, and
• rank each characteristic of the plurality of characteristics based on a score difference between the credit risk score for the applicant and the replacement score for the respective characteristic;
• and
• output a report identifying one or more top ranked characteristics of the plurality of characteristics as the one or more principal adverse action factors for the denial of the credit request of the applicant.