US Patent Application 18324307. ARTIFICIAL INTELLIGENCE ENGINE FOR TRANSACTION CATEGORIZATION AND CLASSIFICATION simplified abstract
ARTIFICIAL INTELLIGENCE ENGINE FOR TRANSACTION CATEGORIZATION AND CLASSIFICATION
Organization Name
Mastercard International Incorporated
Inventor(s)
Sachin Pandey of Gorakhpur (IN)
Siddhesh Dongare of Kalyan (IN)
Dmitriy Kontarev of Allen TX (US)
Brett Ragozzine of Lehi UT (US)
Christopher Brousseau of Provo UT (US)
ARTIFICIAL INTELLIGENCE ENGINE FOR TRANSACTION CATEGORIZATION AND CLASSIFICATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18324307 titled 'ARTIFICIAL INTELLIGENCE ENGINE FOR TRANSACTION CATEGORIZATION AND CLASSIFICATION
Simplified Explanation
- The patent application describes techniques for improving the classification of open banking transactions using a classification model. - The techniques involve receiving raw training data, which includes historical transaction data. - The raw training data is processed by performing a data preparation operation, which involves removing numerical characters, repeating special characters, and accent words from the textual data of each transaction. - Vocabulary training is then performed on the processed training data, including tokenizing the text of each transaction and converting it into a specific format for a transformer model. - The classification model is trained using a transformer model that utilizes the tokenized text. - The trained classification model is stored in a database.
Original Abstract Submitted
Techniques for training a classification model to improve the classification of open banking transactions are presented. The techniques include receiving raw training data from a data source. The raw training data includes historical transaction data made up of a plurality of individual transactions. The raw training data is input into the classification model. The raw training data is processed by performing a data preparation operation on the raw training data. The data preparation operation includes removing numerical characters, repeating special characters, and accent words from the textual data of each transaction. Vocabulary training is then performed on the processed training data, including tokenizing the text of each transaction and converting the tokenized text into a transformer model specific format. The classification model is then trained using a transformer model, which uses the tokenized text. The trained classification model is then stored in a database.
