Automation can be especially useful in reducing the time it takes to review non-standard documentation.Īn automated underwriting system (AUS) can help lenders better understand the nuances of an unconventional borrower’s financial picture. One way in which non-QM lenders can reduce costs – and risk – is by implementing automated mortgage review processes that use artificial intelligence and advanced analytics to streamline mortgage origination, closing, and servicing. Automating Mortgage Review with Artificial Intelligence That process can be time-consuming and often requires manual review. Lenders have to review and verify non-standard documents in order to verify the accuracy and completeness of applications from borrowers. Lenders might, for example, want everything from proof of homeowners and flood insurance to investment income statements and details on principal, interests, taxes, and insurance (PITI) on other properties a borrower owns.įor lenders, meeting underwriting and compliance requirements can be a costly, tedious, and time-consuming process. Non-QM lenders need to consider alternative income documentation, such as personal and business bank statements, and personal financial statements that detail assets and liabilities.Īdditional documentation required often depends on the borrower’s financial situation and the property being purchased. Automation can help with the documentation process and can facilitate aggregating information into one, standardized document.įull documentation typically requires pay stubs, W-2s, and tax returns. That means lenders have to carefully verify income sources, assets, identity and liabilities. Minimizing risk when underwriting non-QM loans requires the ability to fully assess a borrower’s financial condition. But lending to non-QM borrowers still requires due diligence to protect against default. That means there is a growing market of highly qualified non-QM borrowers. Many of those newly independent workers have strong personal balance sheets, but don’t meet the requirements for a qualified mortgage. In addition, an increasing number of gig economy workers is also swelling the ranks of borrowers interested in non-QM loans. The COVID-19 pandemic has disrupted the job market and the number of self-employed is rising as people opt not to return to traditional brick and mortar jobs. Growing Number of Non-traditional Borrowers for Non-QM Loans This gives non-traditional fintechs an opportunity to gain market share. Legacy banks tend to prefer mainstream QM loans, leaving the non-QM lane open for other lenders. Non-QM loans also come into play when borrowers don’t have perfect credit – such as a past bankruptcy – or when they want an interest-only mortgage. The average borrower’s FICO score is about 700 and the average Loan to Value (LTV) ratio is around 70%.īorrowers who apply for non-qualified mortgages are often young, self-employed, or investors interested in purchasing rental or vacation properties. The S&P analysis also underscores the fact that non-QM loans are not necessarily high risk. S&P Global predicts that non-QM loan volume – which slowed during the pandemic – will increase from $12 billion a year to $25 billion by the end of 2021. The combination of a revised DTI limit and government-backed non-QM loans is expected to give non-traditional borrowers more choices. That could expand funding options for non-qualified mortgage borrowers, especially with government-sponsored enterprises, Fannie Mae and Freddie Mac, embracing the new rule. Moreover, a revised qualified mortgage rule issued by the Consumer Financial Protection Bureau establishes a pricing threshold to replace the 43% DTI limit. While QM loans provide lenders with some additional liability protection, there is still a strong market for non-QM loans. New pricing opportunites and improved automation for underwriting promises to help fintechs meet a growing demand for non-QM loans. Lenders in today’s marketplace understand that some customers are viable mortgage loan applicants – even if they don’t meet the consumer protection thresholds defined by Congress in the 2010 Dodd-Frank Act. Homebuyers with erratic income streams, a debt-to-income (DTI) ratio higher than 43%, or a short credit history often have unique financial situations that preclude them from meeting the requirements for a qualified mortgage (QM), but that shouldn’t necessarily rule them out as low-risk borrowers.
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