Systems and methods for using small transformer models in financial transaction understanding

A system for financial transaction understanding, comprising:

• one or more processors;
• one or more memories coupled to the processors and storing instructions that, when executed by the processors, cause the system to:
• receive transaction text and a transaction zipcode;
• execute a rule-based matching module to attempt to identify a merchant;
• upon failure of the rule-based matching module, execute an enhanced string distance module to attempt to identify the merchant;
• upon failure of the enhanced string distance module, invoke a transformer model selected from an encoder-only model and a decoder-based model;
• perform candidate filtering using the transaction zipcode;
• select a candidate merchant from a merchant database using either cosine similarity between embedding vectors produced by the encoder-only model or string similarity between a generated canonical merchant name produced by the decoder-based model and merchant names in the merchant database;
• compute a verification score comprising a similarity score and a confidence score;
• and
• update an enhanced transaction database with enhanced merchant information when the verification score satisfies a threshold.

A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause a processing machine to:

• obtain a training corpus comprising transaction text and corresponding merchant labels;
• train a transformer encoder-only model using contrastive loss to encode both transactions and merchant names into an embedding space and to optimize a similarity margin between positive and negative merchant samples;
• train a transformer decoder-based model using cross-entropy loss to generate canonical merchant names from transaction inputs and to produce confidence scores;
• deploy the encoder-only model and the decoder-based model in a transaction understanding pipeline that first applies rule-based matching and enhanced string distance matching, and upon failure invokes the encoder-only model or the decoder-based model;
• and
• select and verify a merchant match using a zipcode-constrained candidate pool, a similarity score, and a confidence score.