Point of View : Solving the Roomba Problem in Automated Underwriting
Lenders have invested heavily in LOS platforms, automated underwriting engines, income analyzers, and AI-driven tools. On paper, underwriting should be faster and more scalable than ever. In practice, teams still spend a significant portion of every day on document work before any credit logic runs. This paper diagnoses why, quantifies the cost, and shows what true end-to-end automation looks like.
The core issue is structural: most automated underwriting platforms assume a near-perfect input state where documents are present, correctly labeled, attributed to the right borrowers, and organized in the LOS. However, achieving that state is not a result of underwriting automation. It is still done by humans, every day. While automation has reduced keystrokes, it has not reduced file touches.
The solution is not to install a better document management tool or add another integration layer to the LOS. The solution is to fundamentally redefine what automated underwriting means and to insist that any platform claiming that label must be capable of accepting messy, real-world input and producing reliable, decision-ready output without human preparation in between.
The Roomba problem gets in the way of real automation. Underwriters are still responsible for indexing, labeling, and foldering documents before the platform can run, which defeats the goal of automated underwriting.
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