Systems and methods for dynamic vendor and vendor outlet classification

A method for dynamic vendor classification, said method comprising:

• receiving website content from each of a plurality of websites at a dynamic vendor classification system, wherein the website content represents at least a portion of each of the plurality of websites, forming a plurality of website content;
• normalizing the plurality of website content based upon at least one of hypertext markup language tags, encoded content, or a visual comparison of screen shots for each one of the plurality of website content, thereby providing normalized content;
• analyzing the normalized content using a processor based on a congruence analysis to identify content as congruent when the normalized content is the same, analyzing the normalized content based on a similarity analysis to identify content as similar when the normalized content is made up of substantially the same components but is not more than a first predetermined percentage the same, and identifying the normalized content as different when the normalized content is less than a second predetermined percentage the same, wherein the second predetermined percentage is less than the first predetermined percentage;
• identifying a vendor for each of the plurality of websites by comparing categorized websites to stored data for known vendors;
• receiving a request from a customer to access one of the plurality of websites at a dynamic vendor classification module of the dynamic vendor classification system;
• determining whether a vendor of the one of the plurality of websites is trusted, not trusted, or uncertain using a first set of rules when the one of the plurality of websites is congruent to a second one of the plurality of websites and a second set of rules when the one of the plurality of websites is similar to the second one of the plurality of websites;
• and
• when the vendor of the one of the plurality of websites is categorized as uncertain, flagging the vendor for monitoring to gather additional information for a trust analysis.

A set of instructions for execution on a computer and embodied in a non-transitory computer-readable medium, the set of instructions comprising code that performs the steps of:

• receiving website content from each of a plurality of websites at a dynamic vendor classification system, wherein the website content represents at least a portion of the plurality of websites, forming a plurality of website content;
• normalizing the plurality of website content based upon hypertext markup language tags, encoded content, and a visual comparison of screen shots for each one of the plurality of website content, thereby providing normalized content;
• analyzing the normalized content using a processor based on a congruence analysis to identify content as congruent when the normalized content is the same, analyzing the normalized content based on a similarity analysis to identify content as similar when the normalized content is made up of substantially the same components but is not more than a first predetermined percentage the same, and identifying the normalized content as different when the normalized content is less than a second predetermined percentage the same, wherein the second predetermined percentage is less than the first predetermined percentage;
• identifying a vendor for each of the plurality of websites by comparing categorized websites to stored data for known vendors;
• receiving a request from a customer to access one of the plurality of web sites at a dynamic vendor classification module of the dynamic vendor classification system;
• determining whether a vendor of the one of the plurality of websites is trusted, not trusted, or uncertain using a first set of rules when the one of the plurality of websites is congruent to a second one of the plurality of websites and a second set of rules when the one of the plurality of websites is similar to the second one of the plurality of websites;
• and
• when the vendor of the one of the plurality of websites is categorized as uncertain, flagging the vendor for monitoring to gather additional information for a trust analysis.