System and method for fraud verification across user identification formats implementing scanning technologies

A system for fraud verification across user identification formats, the system comprising:

• a processor;
• and
• a memory operatively connected to the processor via a communication interface, the memory storing computer readable instructions, when executed, causes the processor to:
• scan an identification document presented by a customer at a branch office by utilizing a scanning device;
• generate, in response to scanning, a digital image of the identification document;
• transmit the digital image to a server;
• call a first application programing interface (API) to read the digital image from the server and request validation of the digital image with a second API for fraud verification across user identification formats;
• call the second API to transmit the digital image to a Software as a Service (SaaS);
• receive, by the second API, an identification validation response from the SaaS using existing data corresponding to the customer;
• implement an Artificial Intelligence (AI)/Machine Learning (ML) model to generate a confidence score based on predefined rules and historical data;
• determine that the confidence score is equal to or more than a configurable threshold value;
• and
• validate the digital image based on determining that the confidence score is equal to or more than the configurable threshold value.

A non-transitory computer readable medium configured to store instructions for fraud verification across user identification formats, the instructions, when executed, cause a processor to perform the following:

• scanning an identification document presented by a customer at a branch office by utilizing a scanning device;
• generating, in response to scanning, a digital image of the identification document;
• transmitting the digital image to a server;
• calling a first application programing interface (API) to read the digital image from the server and request validation of the digital image with a second API for fraud verification across user identification formats;
• calling the second API to transmit the digital image to a Software as a Service (SaaS);
• receiving, by the second API, an identification validation response from the SaaS using existing data corresponding to the customer;
• implementing an Artificial Intelligence (AI)/Machine Learning (ML) model to generate a confidence score based on predefined rules and historical data;
• determining that the confidence score is equal to or more than a configurable threshold value;
• and
• validating the digital image based on determining that the confidence score is equal to or more than the configurable threshold value.