Machine learning has significantly increased document processing efficiency in the mortgage industry. However, the effectiveness of any technology depends on document quality and a vendor’s ability to train machine learning models on a broad set of document types. As such, it’s wise to be skeptical of broad accuracy claims being made across the industry from start-ups and seasoned vendors alike.…
LoanLogics is often asked for specific examples of where machine learning can make impact along the mortgage value chain and of course, as a regtech provider, we have found practical application in loan quality management. Early application of machine learning technologies are being used for the creation of verified, validated data (or what we’ve termed “purified data”), that powers…
The data requirements needed to manufacture and decision a loan seem to be ever growing, as do regulator and investor expectations. Data is distributed across multiple document types, the loan origination system and a multitude of other sources. All this information needs to be verified and validated to ultimately come to the source of truth and support confidence in loan…
Few would argue closed loan acquisition is complex. Managing risk against faster turn-times, while trying to maintain relationships with sellers surely keeps many of correspondent investors awake at night. Go too fast, increase your risk. Go too slow, stress margins and ultimately lose sellers. How can correspondent investors minimize portfolio risk while going for speed? By bringing intelligent, automated data…
