HIERARCHICAL AND MULTI-LABEL TRANSACTION CLEANSING AND CATEGORIZATION

1 . A method, comprising:

receiving a transaction string associated with payment processing for a transaction by a financial institution (FI);

cleansing and normalizing the transaction string as normalized data;

labeling entities identified in the normalized data;

assigning categories to each entity based on a unique hierarchy associated with each entity using the normalized data to produce a multi-entity labeled and multi-categorized string for the transaction; and

providing the multi-entity labeled and multi-categorized string to a system associated with the FI for subsequent analytics.

2 . The method of claim 1 , wherein cleansing further includes processing a bidirectional encode representations from transformers (BERT) algorithm to convert words in the transaction string in a numerical form while maintaining context of the words within the normalized data.

3 . The method of claim 1 , wherein cleansing further includes embedding semantic data within the normalized data to assist in labeling and to assist in identifying a location for the transaction.

4 . The method of claim 3 , wherein labeling further includes providing the normalized data as input to a first machine learning model (model) and receiving an entity-labeled normalized string as output from the first model.

5 . The method of claim 4 further comprising, adjusting the semantic data by the first model during subsequent iterations of the method to improve on entity identification.

6 . The method of claim 4 , wherein assigning further includes providing the entity-labeled normalized string as input to a second model and receiving the multi-entity labeled and multi-categorized string as output from the second model.

7 . The method of claim 6 , wherein providing the entity-labeled normalized string further includes remapping, by the second model, merchant category codes present in the entity-labeled normalized string to predefined categories.

8 . The method of claim 7 , wherein remapping further includes cascading, by the second model, each labeled entity and the entity-labeled normalized string through a top or a head of a corresponding hierarchy associated with a corresponding entity.

9 . The method of claim 8 further comprising processing the second model as a hierarchical convolutional neural network (HCNN) trained on the hierarchies associated with the entities.

10 . The method of claim 9 further comprising processing the second model as a cascading HCNN that works on each subspace of each corresponding hierarchy until there are no subspaces through which to cascade.

11 . The method of claim 1 further comprising, providing the method as a cloud service that interacts with a payment service of the FI.

12 . A method, comprising:

obtaining a transaction string for a transaction being processed for payment by a financial institution (FI) during the transaction of a customer with a merchant;

identifying organization entities from the transaction string;

assigning categories to each organization entity based on a unique hierarchy associated with each organization entity and based on the transaction string; and

providing a multi-entity labeled and categorization string representing the organization entities and the categories to a system of the FI to associate with the transaction.

13 . The method of claim 12 further comprising, iterating the method for a batch of additional transaction strings associated with additional transactions of the FI.

14 . The method of claim 12 , wherein obtaining further includes processing a natural language processing (NLP) algorithm to convert words in the transaction string into a numeric representation while maintaining a context of the words.

15 . The method of claim 14 , wherein processing further includes processing a word-to-vector (word2vec) algorithm to convert the transaction string into a vector mapped to multidimensional space.

16 . The method of claim 15 , wherein processing further includes embedding semantic information within the vector.

17 . The method of claim 12 , wherein identifying further includes cleansing and adding semantic information to the transaction string creating normalized data and providing the normalized data as input to a first machine learning model (model) and receiving as output, from the first model, an entity label for each of the organization entity.

18 . The method of claim 17 , wherein assigning further includes provide each entity label and the normalized data as input to a second model and receive as output, from the second model, hierarchical categories for each organization entity based on a corresponding hierarchy for a corresponding organization entity.

19 . A system, comprising:

at least one server comprising a processor and a non-transitory computer-readable storage medium;

the non-transitory computer-readable storage medium comprises executable instructions; and

the executable instructions when executed on the processor cause the processor to perform operations comprising:

receiving a transaction string for a transaction being paid through a financial institution (FI) and being processed by a merchant for a customer;

cleansing and normalizing the transaction string into normalized data;

embedding semantic information into the normalized data;

assigning entity labels for organizations identified in the normalized data;

assigning categories for each entity label using a unique hierarchy associated with a corresponding organization and using the normalized data; and

providing a multi-entity and multi-categorized string having the entity labels and the categories to a system of the FI.

20 . The system of claim 19 , wherein the system is an analytics system of the FI.