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Mortgage Lending
How Machine Learning is Revolutionizing the Mortgage Process
12 May 2022
In 2021, the mortgage market grew to $1.61 trillion, marking the highest mortgage borrowing numbers in history. However, by the end of 2022, mortgage demand plunged 13.2% due to climbing interest rates. Whether the industry is seeing unprecedented or fluctuating demand, the mortgage application process often leaves both lenders and borrowers with a desire for faster, improved processes.
Why does mortgage loan origination take so long?
Traditional mortgage lending processes are manual, slow and can feel outdated compared to the seamless digital experiences that have become the norm. Processing a mortgage application is often a tedious and time-consuming endeavor, requiring repetitive manual work and extensive back-and-forth communication. The traditional process places a heavy burden on borrowers and originators alike.
A borrower must be in frequent contact with the loan officer. Details such as a mismatched name or a missing piece of documentation can significantly delay an application’s approval. Human error can also throw an application off course, adding a disproportionate level of complexity and time to the process.
What does an automated mortgage solution look like?
Automation using machine learning accelerates the mortgage origination process, making it more efficient for borrowers and lenders alike. A successful mortgage automation solution begins at the document collection step by classifying borrower-submitted documents. The borrower is able to receive instant feedback so that any potential issues such as missing documents or incorrect statement dates can be quickly resolved. Once all required documents have been uploaded and verified, key data points can be extracted, cleaned, and validated, and provided to the lender. Additional checks such as verifying borrower income and categorizing income, assets, and tax forms can also be automated.
With clean, reliable, structured data, mortgage originators can make more informed, confident lending decisions. Further, a successful automated solution sharply reduces the number of manual processes needed to close the loan. By removing unnecessary manual steps, the likelihood of error is minimized, and the time to process is significantly reduced, allowing borrowers to get approved faster, and lenders to dramatically scale volume.
Manual lending processes also prevent lenders from efficiently processing applications. With varying loan volumes and differing mortgage products, lenders may not always have the staff to handle fluctuating processing volumes. By automating mortgage lending processes using machine learning techniques, mortgage originators can gain the operational agility required to navigate market fluctuations at scale.
How machine learning is enhancing analytics and fraud detection
Mortgage lending fraud continues to be an expensive problem for lenders. In fact, for some lenders, 1 in 20 loans will default, many without a single payment made. Machine learning enables fraud detection to be incorporated into an automated mortgage solution. Reliable fraud detection allows lenders to confidently and quickly approve trusted loans, and avoid unnecessary loss. Ocrolus’ automated fraud detection algorithm, Detect, can identify if a file, such as a bank statement, has been tampered with, and provides rich contextual visualizations and data on which fields (such as account number or account balance) have been modified.
Detailed visualizations allow credit and risk analysts to quickly assess any edits that have been made to a file, and make an informed decision on how to proceed. Additional signals can indicate if a file has been generated by the borrower rather than the bank. Further analytics can flag suspicious signals such as incorrect withholding amounts or determine if transactions do not add up correctly.
Improving the mortgage process with machine learning
Traditional mortgage lending processes are slow and cumbersome, often dependent on manual work, and necessitate back-and-forth communication between the borrower and the lender. These processes create a frustrating and opaque experience for the borrower, are tedious for the lender, and can dramatically limit the ability to scale.
Machine learning can be integrated to improve every stage of the lending process, from origination to closing. Ocrolus’ mortgage automation solution modernizes the process, creating a transparent and efficient workflow for borrowers, and helping lenders make smarter decisions in less time so that they can easily finalize loans ahead of deadlines.
Sylvia
Naples
Sylvia Naples is the Technical Lead on the Machine Learning (ML) Fraud Detection team. The team leverages Ocrolus’ expansive collection of financial documents and advanced ML models to offer contextualized file tampering detection. Before focusing on fraud, Sylvia was a Senior Machine Learning Engineer at Ocrolus. She worked on integrating language models like BERT and BERT NER, developed a classification model that works on mixed documents, and made significant improvements to the accuracy of Ocrolus’ classification and extraction models.
Prior to Ocrolus, she worked as a Machine Learning Engineer to automate online eye exams. Formerly a mathematician, Sylvia loves finding creative and elegant solutions to challenging problems, as well as passing on that passion to the next generation. Sylvia has worked as a mathematics and programming tutor for over 15 years.