Abstract: Techniques for certifying a document and for securely transmitting access to the document across a blockchain network are disclosed. A document, which details results of a soft inquiry executed against a user's credit history, is accessed. A hash algorithm is applied to the document to certify it. The hash algorithm is based on a timestamp indicating when the document was created. Applying the hash algorithm also generates a portable identifier reflecting the document is certified at the time indicated by the timestamp. The portable identifier is added as a record to a blockchain. The record is transmitted to a second computer system using public and private keys.

Abstract: The automated generation of a unique letter or unique letters using one or more context variables for the letter. The contextual variables may represent author characteristics, audience characteristics, tone, word diversification, letter type, and so forth. Different entropy may be used for each letter to thereby generate a unique letter even if the context for the letters is the same. Nevertheless, each unique letter is suitable for the given context. If desired, the automatically generated letter may be further edited, for example, for grammatical, word choice, or legal content. Thus, the letter may appear to be custom drafted by a human for the context, whereas the letter was entirely or substantially computer-generated.

Abstract: A method for taking appropriate action on a credit report for an individual based on analysis of properties of at least some of the trade lines of the credit report. Upon accessing the credit report, each of at least some of the trade lines in the credit report are processed by 1) identifying one or more properties of the subject trade line, 2) performing one or more removal success rate estimations for similar trade lines having at least one like property compared to the subject trade line, and 3) calculating at least one removability index (e.g., an item removal difficulty index or an item removal speed index) for the subject trade line based on the removal success estimation(s). The calculated removability indices are then aggregated for multiple trade lines, and the course of action for the credit report as a whole is determined based on the removability indices.

Abstract: The automated generation of a unique letter or unique letters using one or more context variables for the letter. The contextual variables may represent author characteristics, audience characteristics, tone, word diversification, letter type, and so forth. Different entropy may be used for each letter to thereby generate a unique letter even if the context for the letters is the same. Nevertheless, each unique letter is suitable for the given context. If desired, the automatically generated letter may be further edited, for example, for grammatical, word choice, or legal content. Thus, the letter may appear to be custom drafted by a human for the context, whereas the letter was entirely or substantially computer-generated.

Abstract: A user interface that may be used to assist in the generation of a plan to improve a credit score of an individual. The user interface includes multiple trade line matched set visualizations, each set resulting from matching similar trade lines from multiple credit reports from multiple credit bureaus. Each of at least some of the trade line set visualizations is visually associated with one or more controls that may be interacted with by the user (e.g., by the individual about whom the credit reports relate), to select from an action to be performed with respect to the alleged event. The action may be a request to the credit bureaus to verify the information supporting the trade line, even without challenging the truth of the underlying information.

Abstract: The automated generation of a unique letter or unique letters using one or more context variables for the letter. The contextual variables may represent author characteristics, audience characteristics, tone, word diversification, letter type, and so forth. Different entropy may be used for each letter to thereby generate a unique letter even if the context for the letters is the same. Nevertheless, each unique letter is suitable for the given context. If desired, the automatically generated letter may be further edited, for example, for grammatical, word choice, or legal content. Thus, the letter may appear to be custom drafted by a human for the context, whereas the letter was entirely or substantially computer-generated.

Abstract: A method for taking appropriate action on a credit report for an individual based on analysis of properties of at least some of the trade lines of the credit report. Upon accessing the credit report, each of at least some of the trade lines in the credit report are processed by 1) identifying one or more properties of the subject trade line, 2) performing one or more removal success rate estimations for similar trade lines having at least one like property compared to the subject trade line, and 3) calculating at least one removability index (e.g., an item removal difficulty index or an item removal speed index) for the subject trade line based on the removal success estimation(s). The calculated removability indices are then aggregated for multiple trade lines, and the course of action for the credit report as a whole is determined based on the removability indices.