Machine learning and validation of account names, addresses, and/or identifiers
Systems and methods are disclosed for determining if an account identifier is computer-generated. One method includes receiving the account identifier, dividing the account identifier into a plurality of fragments, and determining one or more features of at least one of the fragments. The method further includes determining the commonness of at least one of the fragments, and determining if the account identifier is computer-generated based on the features of at least one of the fragments, and the commonness of at least one of the fragments.
1. A computer-implemented method for determining if an electronic account identifier is computer-generated, comprising:
receiving the electronic account identifier;
analyzing the electronic account identifier to determine a plurality of fragments comprising hashed or truncated identifier fragments of a predetermined character length range;
comparing the plurality of fragments to determine one or more alphanumeric features of at least one fragment;
comparing the at least one fragment with a second plurality of fragments associated with a plurality of electronic account identifiers to determine a percentile of commonness of the at least one fragment;
determining, by a computer, if the electronic account identifier is computer-generated beyond a predetermined confidence threshold based on the determined one or more features of the at least one fragment;
transmitting the determined computer generated electronic account identifier to a probabilistic classifier model, wherein the probabilistic classifier model is a trained machine learning system; and
training the probabilistic classifier model further based on the transmitted electronic account identifier.
2. The method of claim 1 , wherein determining if the electronic account identifier is computer-generated comprises providing the determined one or more features of the at least one fragment and the percentile of commonness of the at least one fragment to the probabilistic classifier model.
3. The method of claim 1 , further comprising determining one or more features of the received electronic account identifier by counting characters of the electronic account identifier by character type.
4. The method of claim 1 , wherein determining the percentile of commonness of the at least one fragment comprises determining the frequency of occurrence of the at least one fragment relative to a plurality of fragments in a data store.
5. The method of claim 1 , wherein the at least one fragment is truncated to contain only consonants.
6. The method of claim 1 , wherein each fragment, of the plurality of fragments, includes at least two characters, and wherein each character of each fragment is hashed according to character type of the at least two characters.
7. The method of claim 6 , wherein each character type is selected from a group including consonant, vowel, number, and punctuation mark.
8. A system for determining if an electronic account identifier is computer-generated, the system including:
at least one data storage device storing instructions to determine if the electronic account identifier is computer-generated; and
at least one computer processor configured to execute the instructions to perform a method including:
receiving the electronic account identifier;
analyzing the electronic account identifier to determine a plurality of fragments comprising hashed or truncated identifier fragments of a predetermined character length range;
comparing the plurality of fragments to determine one or more alphanumeric features of at least one fragment;
comparing the at least one fragment with a second plurality of fragments associated with a plurality of electronic account identifiers to determine a percentile of commonness of the at least one fragment;
determining, by a computer, if the electronic account identifier is computer-generated beyond a predetermined confidence threshold based on the determined one or more features of the at least one fragment;
transmitting the determined computer generated electronic account identifier to a probabilistic classifier model, wherein the probabilistic classifier model is a trained machine learning system; and
training the probabilistic classifier model based on the transmitted electronic account identifier.
9. The system of claim 8 , wherein determining if the electronic account identifier is computer-generated comprises providing the determined one or more features of the at least one fragment and the percentile of commonness of the at least one fragment to the probabilistic classifier model.
10. The system of claim 8 , wherein the method further includes determining one or more features of the received electronic account identifier by counting characters of the electronic account identifier by character type.
11. The system of claim 8 , wherein determining the percentile of commonness of the at least one fragment comprises determining the frequency of occurrence of the at least one fragment relative to a plurality of fragments in a data store.
12. The system of claim 8 , wherein the at least one fragment is truncated to contain only consonants.
13. The system of claim 8 , wherein each fragment, of the plurality of fragments, includes at least two characters, and wherein each character of each fragment is hashed according to character type of the at least two characters.
14. The system of claim 13 , wherein each character type is selected from a group including consonant, vowel, number, and punctuation mark.
15. A non-transitory computer-readable medium storing instructions that, when executed by a computer processor, cause the computer processor to perform a method for determining whether an electronic account identifier is computer-generated, the method including:
receiving the electronic account identifier;
analyzing the electronic account identifier to determine a plurality of fragments comprising hashed or truncated identifier fragments of a predetermined character length range;
comparing the plurality of fragments to determine one or more alphanumeric features of at least one fragment;
comparing the at least one fragment with a second plurality of fragments associated with a plurality of electronic account identifiers to determine a percentile of commonness of the at least one fragment;
determining, by a computer, if the electronic account identifier is computer-generated beyond a predetermined confidence threshold based on the determined one or more features of the at least one fragment;
transmitting the determined computer generated electronic account identifier to a probabilistic classifier model, wherein the probabilistic classifier model is a trained machine learning system; and
training the probabilistic classifier model further based on the transmitted electronic account identifier.
16. The computer-readable medium of claim 15 , wherein determining if the electronic account identifier is computer-generated comprises providing the determined one or more features of the at least one fragment and the percentile of commonness of the at least one fragment to the probabilistic classifier model.
17. The computer-readable medium of claim 15 , wherein the method further includes determining one or more features of the received electronic account identifier by counting characters of the electronic account identifier by character type.
18. The computer-readable medium of claim 15 , wherein determining the percentile of commonness of the at least one fragment comprises determining the frequency of occurrence of the at least one fragment relative to a plurality of fragments in a data store.
19. The computer-readable medium of claim 15 , wherein the at least one fragment is truncated to contain only consonants.
20. The computer-readable medium of claim 15 , wherein each fragment, of the plurality of fragments, includes at least two characters, and wherein each character of each fragment is hashed according to character type of the at least two characters, and wherein each character type is selected from a group including consonant, vowel, number, and punctuation mark.