Today, every lender is talking about nonqualified mortgages (non-QM), which are often quality loans that don’t qualify under the standards to be purchased or securitized by Fannie Mae and Freddie Mac. The size of the non-QM market dipped from $22 billion to $16.9 billion during the first year of the COVID-19 pandemic but bounced back in 2021.
This segment reached $25 billion last year. According to some mortgage executives, sales volume is expected to land somewhere between $70 billion and $100 billion by the end of 2022, representing a year-over-year increase of 200% to 300%.
Small- and medium-sized lenders can’t scale unless they hire more underwriters. … With automated bank-statement analysis, however, lenders can increase their processing capacity on demand.
Non-QM loans are useful for self-employed workers, including people who own a business or work in the gig economy, shuffling between two or three jobs on their own schedule. Without a traditional W-2 job, these workers often have trouble getting conventional financing for a home purchase. While non-QM loans are usually a good fit for these clients, the process of underwriting these mortgages can be time-consuming and tedious.
Newly available tools can speed up the underwriting process. Lenders and originators will want to understand how these tools can help them close more loans.
Lucrative opportunity
The refinance market is declining in the U.S., in part due to rising interest rates but also because of the limited number of suitable borrowers under the qualified mortgage regulations. In turn, this is leading to growth in non-QM lending because of these products’ attractive interest rates and flexibility.
This presents a lucrative opportunity for lenders and originators to jump into the non-QM loan space and make the best out of the market. Lenders across the country are rapidly scaling either by upgrading current processes and tools, or by implementing new processes and tools to meet the demand. To do this, they’ll have to increase underwriter productivity and improve the borrower experience.
But instead of upgrading entire processes, mortgage companies should identify areas that contribute to the dip in underwriter productivity and fix them to achieve maximum return on investment. Bank-statement analysis is one such area. This step takes the most time in the non-QM loan qualification process. If lenders are thinking about where to spend their technology budget, the answer is automated bank-statement analysis.
Manual process
Every non-QM loan application involves an underwriter going through the borrower’s bank statements before making a lending decision. Since the process is highly manual, it can take anywhere from a day and a half to three days (if all goes well) for a lender to get back to a borrower with a quote. Due to low processor and underwriter productivity levels, only 10 to 14 loans per month can be closed by each full-time employee, according to a 2021 analysis by management consultant McKinsey & Co.
Underwriters must make sure that they don’t commit any errors while analyzing bank statements, such as failing to identify large deposits that are not in sync or evidence of tampering with the statements. The 2008 subprime loan crisis forced financial institutions to implement more stringent steps while investing in mortgage-backed securities. This involves random sampling of loan applications to see if they’re error-free. Failing to meet this requirement due to underwriter errors could result in a lower rating for the lender.
With so much riding on the underwriters, lenders need an automatic bank-statement analysis tool that is fast, accurate and intelligent. By bringing down the time spent on analyzing bank statements and making sure all transactions on the statements are legit, lenders can make the right lending decisions more quickly before generating a loan quote.
In other words, if all non-QM lenders are cars that are racing against each other, then having an automated bank-statement analysis tool is like having a faster engine. It will help a lender stay ahead of the competition. Thanks to advances in artificial intelligence (AI) and mortgage technology, lenders can now equip themselves with a faster engine.
Tremendous results
AI-powered tools use state-of-the-art models to completely automate the bank-statement analysis process. The analysis is 100% accurate and can bring down the processing time from as many as three days to less than 30 minutes. The AI engine processes 99% of the transaction and requires human verification only for information the model can’t make sense of completely. This eliminates the chance of human error altogether and, in turn, helps to make the right lending decisions.
California-based LendSure Mortgage Corp. has seen tremendous firsthand results from this technology. Their underwriters used to spend roughly two to four hours to get the income data from bank statements and then performed analysis of the collated income data. Now they spend as little as 10 to 30 minutes per application, including analysis.
LendSure CEO Joe Lydon said that an automated bank-statement analysis tool “has made our employees happier by automating monotonous work, reducing errors and brought us great speed, accuracy and scale.” Today, LendSure has completely moved away from Microsoft Excel macros and now uses an automated platform for processing bank statements, arriving at the qualified income of a borrower with a click of a button.
Another area where these tools can shine is capacity. Small- and medium-sized lenders can’t scale unless they hire more underwriters. Even if they do, it takes a few weeks or months to see results. With automated bank-statement analysis, however, lenders can increase their processing capacity on demand. They can effortlessly handle the peaks and valleys of market demand with their existing team. This will keep their operating costs in check and will help their existing team hit maximum productivity.
Growing demand
The mortgage industry is expected to originate more than $2.5 trillion in loans each year through 2024, according to the Mortgage Bankers Association. And non-QM loans could make up a good chunk of that. When it comes to the non-QM market, it’s not the fittest who survives the race, it’s the fastest.
Today is a great time to be a lender or originator. This is because there is high demand for non-QM loans and mortgage companies have the necessary tools to leverage this demand to their advantage.
By investing their technology budget in the right areas, such as AI-powered bank-statement analysis, companies can meet this demand with speed and accuracy. With AI-powered loan automation platforms, lenders can increase underwriter productivity, keep their operating costs in check, close more loans and improve the borrower experience. ●
