Systems and methods for determining relative importance of one or more variables in a non-parametric machine learning model

A method for determining a predetermined number of variables in a non-parametric model that influence an outcome of one or more loan applications, the method comprising:

• receiving, from a database, raw values of the variables corresponding to one or more entities;
• processing the raw values of the variables for the entities using the non-parametric model to obtain logit prediction values for the variables for each entity;
• determining a plurality of cumulative distributions for the variables based on the logit values and the number of entities having a specific logit value;
• binning the variables into a plurality of equally sized buckets based on the cumulative distributions;
• determining a mean logit prediction value for each bucket;
• assigning a rank number for each bucket based on the mean logit prediction values;
• compiling a rank of score averages (ROSA) table for the entities based on the logit values and the buckets corresponding to the logit values;
• determining a plurality of variables with the highest rank numbers;
• identifying a definition for each of the plurality of variables;
• generating a report based on the identified definitions;
• and
• declining the one or more loan applications based on the plurality of variables with the highest rank numbers,
• wherein a larger logit prediction value corresponds to a higher risk for the corresponding loan application and a higher rank number, and
• wherein the non-parametric model is at least one of maximum likelihood (ML), k-nearest neighbors (kNN), and support vector machines (SVM).

A system for identifying a number of variables that influence an outcome of one or more loan applications, the system comprising:

• at least one database for storing raw values for one or more variables corresponding to a plurality of individuals;
• at least one non-transitory computer-readable medium configured to store instructions;
• and
• at least one processor configured to execute the instructions to perform operations comprising:
• receiving, from the database, the raw values of the variables corresponding to one or more applicants among the plurality of individuals;
• processing the raw values of the variables for the applicants using a non-parametric model to obtain logit prediction values, the logit prediction values indicating a first degree of risk for a corresponding loan application based on specific variable indicating a second degree of risk for the corresponding loan application based on the variables;
• determining a plurality of cumulative distributions for the variables based on the logit values and a number of entities having a specific logit value;
• binning the variables into a plurality of equally sized buckets based on the cumulative distributions;
• determining a rank number for each bucket based on the logit prediction values of the variables associated with each bucket;
• compiling a rank of score averages (ROSA) table for the entities based on the logit values and the buckets corresponding to the logit prediction values;
• determining a plurality of variables for the corresponding loan applications based on the highest rank numbers;
• identifying one or more reasons associated with the plurality of variables;
• generating a report based on the reasons, and declining the loan application based on the plurality of variables with the highest rank numbers;
• wherein a larger logit prediction value corresponds to a higher risk for the corresponding loan application and a higher rank number,
• wherein the non-parametric model is at least one of maximum likelihood (ML), k-nearest neighbors (kNN), and support vector machines (SVM).