Methods and systems for predicting an upcoming data point associated with an entity

A computer-implemented method, comprising:

• accessing, by a server system, a plurality of encoded features associated with each transaction of a plurality of transactions performed by a cardholder from a database associated with the server system;
• generating, by the server system, a transaction word representation for the cardholder based, at least in part, on the plurality of encoded features associated with each transaction, wherein the transaction word representation comprises one or more words;
• generating, by the server system, a transaction sentence representation based, at least in part, on the one or more words associated with each transaction;
• generating, by a Large Language Model (LLM) associated with the server system, an upcoming transaction representation in the transaction sentence representation based, at least in part, on the transaction sentence representation being applied to the LLM;
• and
• decoding, by the server system, the upcoming transaction representation to obtain one or more features associated with an upcoming transaction based, at least in part, on a set of predefined language rules.

A server system, comprising:

• a communication interface;
• a memory comprising executable instructions;
• and
• a processor communicably coupled to the communication interface and the memory, the processor configured execute the executable instructions to cause the server system to at least:
• access a plurality of encoded features associated with each data point of a plurality of data points corresponding to an entity from a database associated with the server system;
• generate a data point word representation for the entity based, at least in part, on the plurality of encoded features associated with each data point, wherein the data point word representation comprises one or more words;
• generate a data point sentence representation based, at least in part, on the one or more words associated with each data point;
• generate, by a Large Language Model (LLM) associated with the server system, an upcoming data point representation in the data point sentence representation based, at least in part, on the data point sentence representation being applied to the LLM;
• and
• decode the upcoming data point representation to obtain one or more features associated with an upcoming data point based, at least in part, on a set of predefined language rules.

A non-transitory computer-readable storage medium comprising computer-executable instructions that, when executed by at least a processor of a server system, cause the server system to perform a method comprising:

• accessing a plurality of encoded features associated with each data point of a plurality of data points corresponding to an entity from a database associated with the server system;
• generating a data point word representation for the entity based, at least in part, on the plurality of encoded features associated with each data point, wherein the data point word representation comprises one or more words;
• generating a data point sentence representation based, at least in part, on the one or more words associated with each data point;
• generating, by a Large Language Model (LLM) associated with the server system, an upcoming data point representation in the data point sentence representation based, at least in part, on the data point sentence representation being applied to the LLM;
• and
• decoding the upcoming data point representation to obtain one or more features associated with an upcoming data point based, at least in part, on a set of predefined language rules.