Leveraging Deep Learning, Neural Networks, and Data Engineering for Intelligent Mortgage Loan Validation: A Data-Driven Approach to Automating Borrower Income, Employment, and Asset Verification

Abstract

The mortgage industry’s practice of paper-based, manual, and static loan validation is outdated and lacks efficiency. This essay argues for a timely and relevant need for innovation in this space, and presents an in-depth technical and data-driven solution involving the integration of deep learning, neural networks, data engineering, and multiple online sources. The essay also presents a proof of concept and model-ready pilot project in the area of borrower income, employment, and asset verification. Focus is on borrower-provided asset statements, where the applicant banks are screened and queried for the most recent two monthly statements. The essay views this mortgage loan validation automation opportunity in light of several major regulatory changes that will be deeply impacting the finance and mortgage industry over the next decade.