Machine learning classifier based on category modeling
Provided are systems and methods which can use machine learning to draw additional inferences about transaction records from transaction strings. The inferred data can be used to build a classification model configured to map transaction string to predefined categories. In one example, a method may include receiving a file comprising transaction strings corresponding to a plurality of transaction records, executing the machine learning model on the transaction strings to identify a plurality of categories associated with the transaction strings, generating a classifier model that comprises patterns of keywords from the transactions strings mapped to the plurality of identified categories, respectively, and storing the classifier model in the data store.
1 . A computing system comprising:
a data store configured to store a machine learning model; and
a processor configured to:
receive, via an application programming interface (API) call, a file comprising transaction strings corresponding to a plurality of transaction records;
execute a machine learning model on the transaction strings to identify mappings between the transaction strings and a plurality of deposit source categories;
assign a label, by the machine learning model, to each of the transaction strings corresponding to its associated deposit source category as indicated in the identified mappings;
generate, by the machine learning model, a classifier model that comprises respective patterns of keywords from the transaction strings mapped to the plurality of deposit source categories, respectively, based on the identified mappings;
store the classifier model in the data store;
receive, via a second API call, a file including a plurality of additional transaction records, the plurality of additional transaction records corresponding to additional plurality of transaction strings;
execute the classifier model on the plurality of additional transaction strings to identify one of the deposit source categories to assign to each of the additional transaction records and to generate an output file including each of the additional transaction records further labeled with the deposit source category identified by the classifier model; and
execute a second machine learning model on the output file including the plurality of labeled additional transaction strings to identify a counterparty of each additional transaction strings.
2 . The computing system of claim 1 , wherein the processor is further configured to parse the plurality of transaction strings and remove variable features from the parsed transaction strings prior to executing the machine learning model on the transaction strings.
3 . The computing system of claim 2 , wherein the processor is configured to remove one or more of date values, non-word characters, and whitespaces, from the transaction strings to create cleaned transaction strings.
4 . The computing system of claim 1 , wherein the plurality of categories comprises a plurality of deposit sources, and the processor is configured to execute a machine learning classification model on the transaction strings to identify which deposit source from among the plurality of deposit sources is mapped to each transaction string, respectively.
5 . The computing system of claim 1 , wherein the processor is further configured to execute a third machine learning model on the plurality of labeled additional transaction strings to verify an income of a user associated with the plurality of additional transaction strings.
6 . A method comprising:
receiving, via an application programming interface (API) call, a file comprising transaction strings corresponding to a plurality of transaction records;
executing a machine learning model on the transaction strings to identify mappings between the transaction strings and a plurality of categories;
assigning a label, by the machine learning model, to each of the transaction strings corresponding to its associated deposit source category as indicated in the identified mappings;
generating, by the machine learning model, a classifier model that comprises respective patterns of keywords from the transaction strings mapped to the plurality of categories, respectively, based on the identified mappings;
storing the classifier model in a data store;
receiving, via a second API call, a file including a plurality of additional transaction records, the plurality of additional transaction records corresponding to additional plurality of transaction strings;
executing the classifier model on the plurality of additional transaction strings to identify one of the deposit source categories to assign to each of the additional transaction records and to generate an output file including each of the additional transaction records further labeled with the deposit source category identified by the classifier model; and
executing a second machine learning model on the output file including the plurality of labeled additional transaction strings to identify a counterparty of each additional transaction strings.
7 . The method of claim 6 , wherein the method further comprises parsing the plurality of transaction strings and removing variable features from the parsed transactions strings prior to executing the machine learning model on the transaction strings.
8 . The method of claim 7 , wherein the removing comprises deleting one or more of date values, non-word characters, and whitespaces, from the transaction strings, to create cleaned transaction strings.
9 . The method of claim 6 , wherein the plurality of categories comprises a plurality of deposit sources, and the executing comprises executing the classifier model on the transaction strings to identify which deposit source from among the plurality of deposit sources is mapped to each transaction string, respectively.
10 . The method of claim 6 , wherein the method further comprises executing a third machine learning model on the plurality of labeled additional transaction strings and the identified counterparties of the additional transaction strings to verify an income of a user associated with the plurality of additional transaction strings.
11 . A non-transitory computer-readable medium comprising instructions which when executed by a computer cause a processor to perform a method comprising:
receiving, via an application programming interface (API) call, a file comprising transaction strings corresponding to a plurality of transaction records;
executing a machine learning model on the transaction strings to identify mappings between the transaction strings and a plurality of categories;
assigning a label, by the machine learning model, to each of the transaction strings corresponding to its associated deposit source category as indicated in the identified mappings;
generating, by the machine learning model, a classifier model that comprises respective patterns of keywords from the transaction strings mapped to the plurality of categories, respectively, based on the identified mappings;
storing the classifier model in a data store;
receiving, via a second API call, a file including a plurality of additional transaction records, the plurality of additional transaction records corresponding to additional plurality of transaction strings;
executing the classifier model on the plurality of additional transaction strings to identify one of the deposit source categories to assign to each of the additional transaction records and to generate an output file including each of the additional transaction records further labeled with the deposit source category identified by the classifier model; and
executing a second machine learning model on the output file including the plurality of labeled additional transaction strings to identify a counterparty of each additional transaction strings.
12 . The non-transitory computer-readable medium of claim 11 , wherein the method further comprises parsing the plurality of transaction strings and removing variable features from the parsed transactions strings prior to executing the machine learning model on the transaction strings.
13 . The non-transitory computer-readable medium of claim 12 , wherein the removing comprises deleting one or more of date values, non-word characters, and whitespaces, from the transaction strings, to create cleaned transaction strings.
14 . The non-transitory computer-readable medium of claim 11 , wherein the plurality of categories comprises a plurality of deposit sources, and the executing comprises executing a machine learning classification model on the transaction strings to identify which deposit source from among the plurality of deposit sources is mapped to each transaction string, respectively.