The client is a Texas-based, full-service mortgage lender licensed in over 30 U.S. states. With a strong emphasis on affordable housing, the company partners with community-based lenders and credit unions to offer government-backed loans such as FHA, VA, and USDA, along with several proprietary assistance programs customized for first-time homebuyers and borrowers.
The company manages everything from loan origination to correspondent loan purchase, including document management, borrower verification, and compliance workflows. Known for its flexible loan programs and nationwide partnerships, the client plays a crucial role in providing access to sustainable homeownership for low- to moderate-income segments.
As a certified GNMA-approved issuer and seller/servicer for multiple federal loan programs, the lender is subject to stringent documentation and regulatory requirements. This creates operational complexity that demands accuracy, quick turnaround times, and strong workflow visibility.
The client needed a solution to handle borrower documents that often arrive as bundled PDF files — sometimes uploaded at different stages of the loan process. These packages had to be split and indexed by document type and matched to a predefined stacking order.
Deliverables Requested
To meet tight service-level requirements, Flatworld.ai was tasked with building an automated document indexing workflow backed by a Human-in-the-Loop (HIL) system to:
The Flatworld.ai team followed a 5-phased delivery model to deploy indexing and versioning automation using MSuite.
The project began with close collaboration between Flatworld and the client’s operations team to define key indexing rules, expected document types, and LOS integration needs. A delivery plan was created to cover milestones outlining deliverables, configuring MSuite, user acceptance testing (UAT) timelines, production launch, and post-deployment checkpoints.
To customize the indexing system to the client’s workflows, a diverse set of borrower document samples was collected. These were used to train the classification model, with adjustments made to accommodate variations in formatting, layout, and stacking order conventions.
The indexing and versioning engine was deployed in a controlled UAT environment. This allowed the Flatworld team to identify and resolve edge cases — such as mislabeled pages or non-standard formats — before production rollout. Feedback from this phase was used to fine-tune system behavior and reduce false positives.
The indexing and versioning modules were deployed to the production environment, allowing MSuite to begin processing live loan files directly from the client’s Loan Origination System (LOS). Throughout this phase, the processing pipeline was closely monitored for system stability, output accuracy, and consistent performance under real-world conditions.
Post-deployment, the system was continuously monitored and refined. Flatworld implemented planned retraining cycles to support new document types, adapt to changes in stacking requirements, and maintain classification accuracy over time.
While the project was delivered on schedule, several technical challenges added layers of complexity.
The deployment of MSuite brought measurable improvements across the client’s loan processing workflow. By automating key steps in document indexing and versioning, the solution allowed faster, more consistent operations with significantly less manual intervention.
Flatworld.ai’s MSuite helped the lender cut processing times, reduce manual effort, and maintain high levels of accuracy. The addition of a human review layer ensured no critical documents were missed, helping the client stay compliant and consistent, even during busy cycles.
Need to automate loan document processing without compromising accuracy? Connect with Flatworld.ai to build a custom indexing and verification system tailored to your LOS environment.
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