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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 business rules logics, which can both eliminate manual tasks and result in productivity increases across the variety of loan files reviews that lenders must perform.
Currently, most lenders complete loan audits by using automated checklist and human staff to “check the checker.” By using machine learning tools instead, lenders are able evaluate loan quality more comprehensibly and in a shorter period of time, leveraging automated audit tests and refocusing auditors on exception handling and higher value risk management tasks.
Across audit types (pre-close, post-close, HMDA review, TRID analysis, etc…) lenders can substantially increase the number loan file reviews they are able to perform. LoanLogics audit services professionals and technology clients have already experienced:
- 7-8 post close loan reviews/ person per day
- 18+ compliance reviews/ person per day
- >90% automation of HMDA loan file reviews
Future applications for the expanded use of “purified data” may be in the business intelligence it provides for the evaluation of lending practices. Because machine learning is able to classify greater numbers of loan documents and capture more purified forms of data, lenders can “slice and dice” their data and carefully analyze it to detect lending bias and other fair lending issues. This essentially gives lenders the power to understand and fix problems in their lending practices before they reach the attention of regulators.
Properly leveraged, AI and machine learning tools can also help lenders and servicers take full advantage of the digital mortgage, which has the added benefit of improving the customer experience and lowering loan costs. This requires the elimination of behind the scenes, manual processes to validate and verify borrower data. Instead, automation can analyze information as quickly as it is collected, removing the imbalance between the efficiency of front-end and back-end processes. It can also eliminate how frequently borrowers are asked for the same information twice or more, avoiding frustration, lowering costs and increasing speed in the process.
Ultimately, the landscape becomes wide open for the development of even more sophisticated applications of AI and machine learning. These can be used to further automate decisions throughout the mortgage value chain, revolutionizing processes and tasks from the point of sale to servicing. Powering these applications with accurate, robust, validated data will help to increase the confidence in the decisions being automated through AI, while improving the consistency of meeting investor and regulatory guidelines. Here are just a few examples:
- Powering applications that help guide borrowers through the application process.
- Powering the use of more sophisticated credit models and underwriting decisions.
- Analyzing trends and patterns that can predict default and delinquency.
- Identifying when borrowers could be prime candidates for refinancing or a home equity product
Machine learning technology is not theory—it has been proven to be critically effective at identifying and classifying loan documents and enabling the extraction of more data from loan files. Comparison of thousands of data elements across a variety of sources can then be done with incredible speed and accuracy. As a mortgage regtech provider forging the way in this space, we encourage you to learn more about its application with us.
*The content of this blog is tied to one of LoanLogics’ recent white papers, “Big” AI Driven by Today’s Machine Learning. Download the full insights on the power of AI and Machine Learning here.
