Mortgage Processing Revolution: Using AI Underwriting Platform

Business Challenge

Mortgage lenders are under much pressure due to the massive influx of unstructured and structured documents. Such complexity is increased because of the precise extraction of data, regulatory requirements that change periodically, and speedy loan approval requirements. Paper-based methods' time-consuming, cumbersome, and erroneous nature renders them inadequate, as they are time-consuming and prone to human error, leading to processing delays and ultimately affecting customer satisfaction.

Key challenges included:

Mastery of Document Forms

A multitude of mortgage forms exist. Some PDFs and scanned images, and some are hand-filled. Such a variety of documents can easily bottleneck the processes that involve humans.

Data Extraction and Validation

From loan documents, there are significant data points, names, addresses, income data, and loan conditions, which cannot be retrieved effectively and without making errors manually.

Regulatory Compliance

The processing of documents must comply with local, state, and federal regulations to avoid costly penalties and mitigate risk.

Reducing Human Intervention

Reducing human intervention was necessary to speed up the underwriting process and minimize errors and processing delays while ensuring accurate data extraction and compliance.

Our Solution

To solve these problems, 4Labs designed an AI-based underwriting platform that automates the entire lifecycle of document processing. The solution uses state-of-the-art machine learning and NLP algorithms to scan, digitize, classify, and analyze mortgage documents, accelerating the review of documents while maintaining accuracy and compliance.

Solution Objectives

Objective 1

To design a web application that scales and is easy to use to fully automate the document processing cycle, from data validation extraction to approval.

Objective 2

The solution should accommodate diverse client workflows, including user roles and document handling requirements, to make it adaptable across various institutions.

Objective 3

Reduce manual intervention and automate routine tasks such as document labeling, data extraction, and compliance checks, thereby reducing errors and enhancing processing speed.