Method and system for correcting errors in consumer reporting

A system for correcting errors in consumer reporting, the system comprising:

• one or more processors;
• a communication network;
• a non-transitory computer-readable memory communicatively coupled to the one or more processors and the communication network and storing thereon instructions that, when executed by the one or more processors, cause the system to:
• train a first machine learning model for identifying incorrect errors in consumer credit reporting using (i) a first set of non-sensitive private information (non-SPI) consumer credit information from statements including incorrect value errors, and (ii) a second set of non-SPI consumer credit information from statements that do not include incorrect value errors;
• train a second machine learning model for correcting incorrect value errors in consumer credit reporting using original values and changed values for previously corrected errors;
• obtain a secure data file including a third set of non-SPI consumer credit information for a consumer related to one or more products;
• generate a non-SPI consumer identifier corresponding to and referencing the third set of non-SPI consumer credit information;
• receive, from a user, a request for the third set of non-SPI consumer credit information of the consumer, the request including the non-SPI consumer identifier;
• in response to verifying that the user has permission to access the third set of non-SPI consumer credit information:
• parse the secure data file to identify one or more particular data fields, and for each particular data field, a subset of the third set of non-SPI consumer credit information corresponding to each particular data field;
• apply the subset of the third set of non-SPI consumer credit information for each particular data field to the first machine learning model to identify an incorrect value error in the third set of non-SPI consumer credit information;
• apply the incorrect value error to the second machine learning model to automatically correct the incorrect value error;
• and
• present an indication of the identified incorrect value error and a corrected value for the incorrect value error along with the subset of the third set of non-SPI consumer credit information corresponding to the identified incorrect value error to be displayed on a client device.

A non-transitory computer-readable memory storing instructions thereon that, when executed by one or more processors, cause the one or more processors to:

• train a first machine learning model for identifying incorrect value errors in consumer credit reporting using (i) a first set of non-sensitive private information (non-SPI) consumer credit information from statements including incorrect value errors, and (ii) a second set of non-SPI consumer credit information from statements that do not include incorrect value errors;
• train a second machine learning model for correcting incorrect value errors in consumer credit reporting using original values and changed values for previously corrected errors;
• obtain a secure data file including a third set of non-SPI consumer credit information for a consumer related to one or more products;
• generate a non-SPI consumer identifier corresponding to and referencing the third set of non-SPI consumer credit information;
• receive, from a user, a request for the third set of non-SPI consumer credit information of the consumer, the request including the non-SPI consumer identifier;
• and
• in response to verifying that the user has permission to access the third set of non-SPI consumer credit information:
• parse the secure data file to identify one or more particular data fields, and for each particular data field, a subset of the third set of non-SPI consumer credit information corresponding to each particular data field;
• apply the subset of the third set of non-SPI consumer credit information for each particular data field to the first machine learning model to identify an incorrect value error in the third set of non-SPI consumer credit information;
• apply the incorrect value error to the second machine learning model to automatically correct the incorrect value error;
• and
• present an indication of the identified incorrect value error and a corrected value for the incorrect value error along with the subset of the third set of non-SPI consumer credit information corresponding to the identified incorrect value error to be displayed on a client device.