Systems and methods for processing machine learning attributes are disclosed. An example method includes: identifying a user transaction associated with a set of transaction attributes and a first transaction status; selecting, based on a risk evaluation model, a first plurality of transaction attributes from the set of transaction attributes; modifying a first value of a first transaction attribute in the first plurality of transaction attributes to produce a first modified plurality of transaction attributes; determining, based on the risk evaluation model, that the first modified plurality of transaction attributes identify a second transaction status different from the first transaction status; and in response to the determining, identifying the first transaction attribute as a risk attribute associated with the user transaction.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system, comprising: a non-transitory memory; and one or more hardware processors coupled to the non-transitory memory and configured to execute instructions to cause the system to perform operations comprising: identifying a user transaction associated with a set of transaction attributes and a first transaction status; selecting, based on a risk evaluation model, a first plurality of transaction attributes from the set of transaction attributes; modifying a first value of a first transaction attribute in the first plurality of transaction attributes to produce a first modified plurality of transaction attributes; determining, based on the risk evaluation model, that the first modified plurality of transaction attributes identifies a second transaction status different from the first transaction status; and in response to the determining, identifying the first transaction attribute as a risk attribute associated with the user transaction. 2. The system of claim 1, wherein the operations further comprise: selecting, based on the risk evaluation model, a second plurality of transaction attributes from the set of transaction attributes; modifying a second value of a second transaction attribute in the second plurality of transaction attributes to produce a second modified plurality of transaction attributes; determining, based on the risk evaluation model, that the second modified plurality of transaction attributes identifies the second transaction status; and in response to the determining, identifying the second transaction attribute as a risk attribute associated with the user transaction. 3. The system of claim 1, wherein the operations further comprise: receiving a user query of one or more risk attributes; and wherein identifying the user transaction is responsive to receiving the user query. 4. The system of claim 3, wherein the operations further comprise: withholding the first plurality of transaction attributes, less the first transaction attribute, from being provided responsive to the user query. 5. The system of claim 1, wherein the operations further comprise: modifying values of a predefined number of transaction attributes in the first plurality of transaction attributes to produce the first modified plurality of transaction attributes. 6. The system of claim 1, wherein the operations further comprise: identifying less than a predefined number of transaction attributes form the first plurality of transaction attributes as risk attributes. 7. The system of claim 1, wherein the operations further comprise: identifying training data attributes based on which the risk evaluation model is constructed; and selecting the first plurality of transaction attributes based on the training data attributes. 8. The system of claim 7, wherein the first plurality of transaction attributes match the training data attributes. 9. A method, comprising: identifying a user transaction having a predefined transaction risk level; selecting, based on a risk evaluation model, a first plurality of transaction attributes associated with the user transaction; modifying a first value of a first transaction attribute in the first plurality of transaction attributes to produce a first modified plurality of transaction attributes; evaluating the first modified plurality of transaction attributes based on the risk evaluation model to determine a modified transaction risk level; determining that a difference between the modified transaction risk level and the predefined transaction risk level is greater than a predefined amount; and in response to the determining, identifying the first transaction attribute as a risk attribute associated with the user transaction. 10. The method of claim 9, further comprising: selecting, based on the risk evaluation model, a second plurality of transaction attributes associated with the user transaction; modifying a second value of a second transaction attribute in the second plurality of transaction attributes to produce a second modified plurality of transaction attributes; evaluating the second modified plurality of transaction attributes based on the risk evaluation model to determine a second modified transaction risk level; determining that a second difference between the second modified transaction risk level and the predefined transaction risk level is greater than the predefined amount; and in response to the determining, identifying the second transaction attribute as a second risk attribute associated with the user transaction. 11. The method of claim 10, wherein the first plurality of transaction attributes and the second plurality of transaction attributes share no overlapping transaction attributes. 12. The method of claim 10, further comprising: withholding the first plurality of transaction attributes and the second plurality of transaction attributes, except the first transaction attribute and the second transaction attribute, from being provided responsive to a user associated with the user transaction. 13. The method of claim 9, further comprising: modifying values of a predefined number of transaction attributes in the first plurality of transaction attributes to produce the first modified plurality of transaction attributes. 14. The method of claim 1, further comprising: limiting a total number of transaction attributes identified as risk attributes associated with the user transaction to a predefined number. 15. The method of claim 1, further comprising: identifying training data attributes based on which the risk evaluation model is constructed; and selecting the first plurality of transaction attributes based on the training data attributes. 16. The method of claim 15, wherein the first plurality of transaction attributes match the training data attributes. 17. The method of claim 15, wherein modifying the first value of the first transaction attribute in the first plurality of transaction attributes includes changing the first value by a predefined amount. 18. A non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations comprising: identifying a user transaction having a predefined transaction risk level; selecting, based on a risk evaluation model, a first plurality of transaction attributes associated with the user transaction; modifying a first value of a first transaction attribute in the first plurality of transaction attributes to produce a first modified plurality of transaction attributes; calculating a modified transaction risk level based on applying the first modified plurality of transaction attributes to the risk evaluation model; determining that a difference between the modified transaction risk level and the predefined transaction risk level is greater than a predefined amount; and in response to the determining, identifying the first transaction attribute as a risk attribute associated with the user transaction. 19. The non-transitory machine-readable medium of claim 18, wherein the first plurality of transaction attributes comprise at least one of: a transaction amount, a user-registered geographical location, a network location of a user device on which the user transaction is conducted, an item of interest associated with the user transaction, a user transaction frequency level, or an average transaction amount. 20. The non-transitory machine-readable medium of claim 18, wherein the operations further comprise: identifying the user transaction in response to a transaction denial or suspension.