The automated financial analysis uses technology to assess risks and analyze loans. This type of analysis can significantly speed up the loan approval process compared to manual analysis.
Definition of Automated Financial Analysis
The automated financial analysis employs technology known as automated analysis systems to evaluate risks and analyze loans. It can make the loan approval process faster and easier for both lenders and borrowers – it’s safe to say that automated financial analysis brings the mortgage process into the 21st century.
How Automated Financial Analysis Works
Automated financial analysis is considered an algorithm that makes a loan decision – or at least a recommendation for a loan decision,” explains Aaron Dorn, Chief Executive Officer of Studio Bank in Nashville, Tennessee, in an email to The Balance. “The program or application is fed key data points, then uses formulas to decide whether the loan is in the best interest of the bank and the borrower.”
Artificial intelligence (AI) is already taking the lead in other financial sectors, such as the investment industry. For example, IBM claims that AI-powered trading platforms (with no human intervention) can execute thousands to millions of transactions daily.
However, at least for now, humans and AI work together in the mortgage approval process. “AI evaluates eligibility and risk factors related to income and assets, debt-to-income ratio, loan-to-value ratio, and credit history, and assists in determining the documents that will be required to verify the data,” said Melinda Whelner, CEO of United Wholesale Mortgage, in an email to The Balance.
Then, according to her explanation, the automated underwriter uses the results of the automated analysis system to guide the final process of the loan credit analysis. “The underwriter ensures that the data entered into the system is accurate, that all required documents have been collected and reviewed, and that the borrower is qualified for a mortgage loan,” Whelner said.
Types of Automated Financial Analysis Systems
Technological advancements have led many lenders to adopt automated analysis systems. For instance, in 2015, Quicken Loans launched Rocket Mortgage, the first completely digital mortgage process, and by the end of 2020, 98% of all of the company’s loans utilized this technology.
In October 2020, the U.S. Department of Housing and Urban Development (HUD) announced its own automated analysis system for the agency’s loans.
“Fannie Mae’s Desktop Underwriter (DU) and Freddie Mac’s Loan Product Advisor (LPA) are the two main automated analysis systems,” Whelner said. “Lenders use either of these systems, or both, to determine if the loan meets Fannie Mae or Freddie Mac’s eligibility requirements.”
Automated Financial Analysis vs Manual Analysis
There are clear advantages of automated analysis over manual analysis. “Automated analysis systems help create speed and efficiency in the credit analysis process, allowing for better decisions by underwriters,” Whelner said. “Automated systems can analyze data quickly and very consistently, providing comprehensive feedback and action items for underwriters to complete the credit analysis process.”
Automated analysis can help reduce paperwork and the time spent reviewing each application, allowing lenders to approve more mortgages and use their time more efficiently.
However, speed is not always a good thing, nor is automated financial analysis a perfect solution. “It’s designed for the benefit of lenders,” but that doesn’t mean it’s always ideal for borrowers,” Dorn noted.
Like any other computer program, the automated financial analysis system relies only on the available data and the programmed calculations to perform. “So, if there is bad data, like errors in a credit report or missing income, it will automatically produce a bad outcome,” Dorn said. For this reason, he prefers manual analysis. “Manual analysis can be slower, but it comes with the benefit of having a human on the other side of the paperwork.”
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Automated analysis systems face limitations that handwritten assessments do not encounter. “For example, handwritten assessments can also take into account considerations that may not be factored into algorithmic calculations, such as community resources and unusual circumstances like natural disasters, hospital stays, global pandemics, and more,” Dorn said.
There are certain scenarios that may prevent the use of automated analysis. “Manual analysis may be needed for some loans where there are matters in the borrower’s file that the automated analysis system cannot assess,” Wilner said. When this occurs, the writer will use manual analysis principles – which may differ from the principles of the automated analysis system – and require the borrower to provide additional documentation.
Source: https://www.thebalancemoney.com/what-is-automated-underwriting-5189934
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