AI Powered Document Automation in Mortgage Processing Scaling Classification and Extraction

Abstract

The article reviews the application of machine learning based on the automation of the enterprise document systems to be applied to scale up mortgages services by classification and retrieval of mortgage information from documents with a processing volume of more than a million pages in a day. The system can implement a cloud-native and microservice architecture to deliver 98 percent accuracy in classification and more than 85 percent in field extraction with document integration that can support over 700 types of documents.  Rather, it will cut the amount of time devotable to the analysis of manuals by 60 percent and the level of compliance preparation by 40 percent. Multimodal models are high performance learned pipelines that can easily be distributed (using Redis and Kafka), scalable, and economical. Companied with the findings, it can be proposed that the pace, precision and scale are greatly improved with the automation of the mortgage document proceedings using AIs.