Ocrolus

Mortgage lenders lose time and margin to the same bottleneck on every loan: manually reviewing documents, calculating income, chasing discrepancies, and managing conditions. Ocrolus eliminates that bottleneck with a connected automation suite that covers the full document workflow from intake to clear-to-close.

It starts with classification and indexing. The platform automatically identifies and organizes incoming borrower documents, including pay stubs, W-2s, tax returns, bank statements, rental schedules, and supports over 2,200 document types, so processors aren't spending time sorting and labeling files by hand.

From there, Ocrolus calculates income across borrower types. For wage earners, it pulls and structures data from pay stubs and W-2s. For self-employed borrowers and rental income, it analyzes tax returns and schedules with built-in Fannie Mae integration, applying agency guidelines directly so underwriters don't have to rebuild calculations from scratch.

The platform then compares extracted document data against the 1003 application to flag discrepancies, including mismatched names, address history gaps, and income inconsistencies, before they turn into conditions or audit findings. Lenders catch problems early instead of discovering them deep in the pipeline.

Finally, Ocrolus automates the condition lifecycle itself. Rather than creating and manually tracking which conditions have been issued, which have been received, and which are still outstanding, the platform generates conditions and manages the workflow end-to-end, reducing the back-and-forth between processors, underwriters, and borrowers that drags out cycle times.

The result is a leaner, faster operation. Lenders handle more volume without adding headcount, underwriters focus on judgment calls instead of data entry, and loans move to closing with fewer touches and fewer surprises.