Systems and methods for optimized transaction processing

A system comprising at least one computer including a processor, wherein the at least one computer is configured to:

• receive a plurality of inputs at a payload engine;
• execute a machine learning engine, wherein the machine learning includes a machine learning model, and wherein the machine learning model is configured to generate output based on the plurality of inputs;
• execute a rules engine, wherein the rules engine includes a logic tree based on the plurality of inputs;
• receive, at the payload engine, a transaction and associated transaction details;
• generate, by the machine learning engine and based on the transaction, the associated transaction details, and the plurality of inputs, a first transaction processing parameter;
• generate, by the rules engine, and based on the transaction, the associated transaction details, and the plurality of inputs, a second transaction processing parameter;
• adhere the first transaction processing parameter and the second transaction processing parameter to one or more input rules to form a transaction payload;
• parameterize the transaction payload to an API method exposed by the transaction processing engine and called by the payload engine;
• and
• execute, by the transaction processing engine, a customized transaction processing route specific to the transaction according to the transaction payload of the API method.

A non-transitory computer readable storage medium, including instructions stored thereon, which instructions, when read and executed by one or more computer processors, cause the one or more computer processors to perform steps comprising:

• providing a plurality of inputs to a payload engine;
• providing a machine learning engine, wherein the machine learning includes a machine learning model, and wherein the machine learning model is configured to generate output based on the plurality of inputs;
• providing a rules engine, wherein the rules engine includes a logic tree based on the plurality of inputs;
• receiving, at the payload engine, a transaction and associated transaction details;
• generating, by the machine learning engine and based on the transaction, the associated transaction details, and the plurality of inputs, a first transaction processing parameter;
• generating, by the rules engine, and based on the transaction, the associated transaction details, and the plurality of inputs, a second transaction processing parameter;
• adhering the first transaction processing parameter and the second transaction processing parameter to one or more input rules to form a transaction payload;
• parameterizing the transaction payload to an API method exposed by the transaction processing engine and called by the payload engine;
• and
• executing, by the transaction processing engine, a customized transaction processing route specific to the transaction according to the transaction payload of the API method.