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 account identifier is computer-generated, comprising:
receiving the account identifier;
analyzing hashed or truncated identifier fragments of the account identifier to determine one or more tokens, the hashed or truncated identifier fragments having a predetermined fragment size, the fragment size automatically determined by a training data set;
comparing the one or more tokens and the fragments of the account identifier to other fragments associated with a plurality of other account identifiers to determine common occurrences; and
determining, by a computer, if the account identifier is computer-generated based on the determined fragment occurrences exceeding a predetermined confidence threshold, wherein the predetermined confidence threshold is based on a size of the other fragments associated with the plurality of the other account identifiers.
2 . The method of claim 1 , wherein determining if the account identifier is computer-generated comprises providing the determined one or more tokens of the common occurrences to a probabilistic classifier model.
3 . The method of claim 1 , further comprising determining one or more tokens of the received account identifier by counting characters of the account identifier by character type.
4 . The method of claim 1 , wherein determining common occurrences comprises determining a frequency of occurrence relative to a plurality of fragments in a data store.
5 . The method of claim 1 , wherein the fragments are 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 account identifier is computer-generated, the system including:
at least one data storage device storing instructions to determine if the account identifier is computer-generated; and
at least one computer processor configured to execute the instructions to perform a method including:
receiving the account identifier;
analyzing hashed or truncated identifier fragments of the account identifier to determine one or more tokens, the hashed or truncated identifier fragments having a predetermined fragment size, the fragment size automatically determined by a training data set;
comparing the one or more tokens and the fragments of the account identifier to other fragments associated with a plurality of other account identifiers to determine common occurrences; and
determining, by a computer, if the account identifier is computer-generated based on the determined fragment occurrences exceeding a predetermined confidence threshold, wherein the predetermined confidence threshold is based on a size of the other fragments associated with the plurality of the other account identifiers.
9 . The system of claim 8 , wherein determining if the account identifier is computer-generated comprises providing the determined one or more tokens of the common occurrences to a probabilistic classifier model.
10 . The system of claim 8 , wherein the method further includes determining one or more tokens of the received account identifier by counting characters of the account identifier by character type.
11 . The system of claim 8 , wherein determining common occurrences comprises determining a frequency of occurrence relative to a plurality of fragments in a data store.
12 . The system of claim 8 , wherein the fragments are 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 account identifier is computer-generated, the method including:
receiving the account identifier;
analyzing hashed or truncated identifier fragments of the account identifier to determine one or more tokens, the hashed or truncated identifier fragments having a predetermined fragment size, the fragment size automatically determined by a training data set;
comparing the one or more tokens and the fragments of the account identifier to other fragments associated with a plurality of other account identifiers to determine common occurrences; and
determining, by a computer, if the account identifier is computer-generated based on the determined fragment occurrences exceeding a predetermined confidence threshold, wherein the predetermined confidence threshold is based on a size of the other fragments associated with the plurality of the other account identifiers.
16 . The computer-readable medium of claim 15 , wherein determining if the account identifier is computer-generated comprises providing the determined one or more tokens of the fragments and the common occurrences to a probabilistic classifier model.
17 . The computer-readable medium of claim 15 , wherein the method further includes determining one or more tokens of the received account identifier by counting characters of the account identifier by character type.
18 . The computer-readable medium of claim 15 , wherein determining common occurrences comprises determining a frequency of occurrence relative to a plurality of fragments in a data store.
19 . The computer-readable medium of claim 15 , wherein the fragments are 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.