Computer-implemented method and system for analysis and validation of consistency across documents including images and language data using artificial intelligence (AI)

A computer-implemented method for providing efficient, systematic artificial intelligence (AI)-driven quality assurance (QA) assessment on damage claims based on an analysis and multiple consistency checks across claims data from different processing stages and against relevant policy, regulation, and guideline information, the method comprising:

• receiving, by an AI-based claims QA agent including instructions stored in non-transitory memory of a computer system and executable by a processor of the computer system, claims data comprising a photo document, a claim document, and a reference document associated with a damaged property;
• extracting, by the AI-based claims QA agent, one or more images and a textual description of the damaged property from the photo document;
• processing, by the AI-based claims QA agent, the one or more images using a computer vision model to extract first damage information associated with the damaged property;
• processing, by the AI-based claims QA agent, the textual description using a first machine learning (ML) model to extract second damage information associated with the damaged property;
• validating, by the AI-based claims QA agent, an accuracy of the photo document based on a consistency between the first damage information and the second damage information to generate a first consistency check result;
• combining, by the AI-based claims QA agent, the first damage information and the second damage information into third damage information;
• processing, by the AI-based claims QA agent, the claim document using a second ML model to extract claim information associated with the damaged property, wherein the claim information comprises a geographical location of the damaged property and one or more line items associated with restoring the damaged property;
• processing, by the AI-based claims QA agent, the reference document using a third ML model to extract contextual information associated with at least one of a policy, a regulation, or a guideline associated with a specific geographical location;
• validating, by the AI-based claims QA agent, an accuracy of the claim document based on a consistency of the claim information against the contextual information, and the combined third damage information to generate a second consistency check result;
• initiating, by the AI-based claims QA agent, a large-language model (LLM) to generate, based on the first consistency check result, the second consistency check result, and the reference document, a QA report for the claims data;
• receiving, by the AI-based claims QA agent, from the LLM, the QA report indicating an issue in at least one of the photo document, the claim document, or the reference document;
• and
• initiating, by the AI-based claims QA agent, based on the QA report, updating the at least one of the photo document, the claim document, or the reference document to correct the issue.