Executive Summary
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.
Methodology
The Agile development methodology ensured the solution was continually refined and improved with real-world feedback. The process was divided into key phases, which include the following:
Discovery Phase
The team worked with key stakeholders to identify pain points and challenges in current workflows. Clear project goals were established, and a roadmap for development was outlined.
Development Phase
The design of the AI platform with an architecture that is sound and scalable and capable of adding support for later growth. Train machine learning models in this stage to accurately capture complex mortgage documents.
Testing Phase
Manual and comprehensive automated testing done to ensure perfect performance across various environments for the client. API was also tested for complete integration with the legacy systems.
Deployment and Feedback Phase
After deployment, the platform was tested for any error. Real-time feedback was acquired from the end-users, which aided in refining the system to fulfill the operational need.
Results
The underwriting AI-powered solution resulted in remarkable results on several KPIs:
Productivity Enhancement:
The loan processing time for documentation was below 45%. This significantly accelerates the entire process for mortgage approvals while allowing lenders to serve more customers within the shortest time.
Higher Accuracy Rate:
Automated error minimization occurred with data extraction and document processing, making way for a cleaner and more authentic outcome.
- Security and Compliance- The SOC 2-compliant security, privacy, and general compliance in each processed document from the platform resulted in reducing violations and risk events that would occur with these violations.
- Customer Satisfaction: The processing is faster, resulting in quicker approval of loans and less paperwork. It improved the satisfaction of borrowers and increased customer retention.
- Cost Savings: Even with fewer operational costs in labor as well as error costs, lenders benefited from automating manual workflows.