Mortgage horror stories abound. The application process can demoralize applicants if they make a mistake. Streeteasy.com wrote about a problem one potential homebuyer faced.
After an applicant’s loan was approved, he sold his comic book collection for $35,000. When the lending institution saw the $35,000 deposit, it “raised red flags and caused a huge delay in closing since the client was unable to document where the funds had come from.”
This cautionary tale is one of the common obstacles that can cause delays when applying for a mortgage. Homebuyers may not understand all the rules or the jargon. Then, there is the overwhelming amount of paperwork, such as pay stubs, W-2s, 1099s, tax returns, property appraisals, clean titles, bank statements and investment records.
“The mortgage industry, however, is slowly embracing artificial intelligence to create a more personalized, efficient and less confusing homebuying journey.”
The mortgage industry, however, is slowly embracing artificial intelligence (AI) to create a more personalized, efficient and less confusing homebuying journey. AI systems should flag issues — such as a sizable undocumented deposit — quicker and warn the homebuyer.
The complexity of mortgages often stems from the traditional processes involved, which include several steps and many opportunities for miscommunication and delay. With product and pricing engines, which use AI and machine learning algorithms, homebuyers can expect more competitive loan options and better interest rates, while lenders should have less risk.
Complicated process
Let’s begin by examining the conventional mortgage process, which can create aggravation and pressure for the buyer, especially when trying to close on a purchase and facing the possibility of the mortgage falling through. The loan application process contains several steps.
These include getting pre-qualified, applying for the mortgage, undergoing underwriting and approval. Homebuyers must also satisfy conditions, such as getting an appraisal, securing a clean title and acquiring homeowners insurance.
These steps take time, and obtaining homeowner’s insurance in some regions, like Florida, is becoming difficult. To make matters worse, applicants sometimes have to deal with multiple mortgage officers, which can lead to conflicting information and additional delays.
AI brings numerous advantages to the mortgage process, offering personalized tools that benefit borrowers and lenders. But there are some key features that AI-powered underwriting provides.
Digital underwriting
Have you ever had to wait for a call back from a loan officer? Chatbots and virtual assistants, which use natural language processing technology, provide applicants with instant responses that may be more accurate than talking to a human loan officer. This eliminates the need to wait for responses from busy loan officers.
These chatbots and virtual assistants help lead the borrower through the entire process, answering questions all along the way. AI chatbots can save operational expenses on staffing or free up time for current staff to concentrate on more detailed applications.
An AI-powered mortgage lending platform creates a centralized communication center between all concerned parties: borrowers, lenders, real estate agents and other involved professionals, which is more efficient and transparent. AI automates updates and notifications to keep borrowers informed about the status of their applications and upcoming milestones or provide a list of issues to avoid. Borrowers benefit from enhanced transparency, while lenders have a central document hub that is easy to track.
AI-powered underwriting systems include instant verification with instant document processing, data extraction, data analysis and cross- referencing, fraud detection, risk assessment and real-time decision- making. Machine learning algorithms and deep learning neural networks can instantly analyze documents, such as pay stubs, bank statements and tax returns, to automate income and asset verification, dramatically cutting the time needed for approval. This speeds up approval and reduces the paperwork for borrowers while saving time and minimizing human errors for lenders.
Risk assessment
AI technology analyzes structured and unstructured data and reviews credit history and other factors to determine a borrower’s risk more accurately and faster. Machine learning models, which train on large datasets, can identify patterns that make borrowers more credit-worthy. Deep learning models analyze complex patterns, including non-traditional information and relationships, with their neural networks to enhance lenders’ accuracy.
AI offers a variety of ways to detect possible fraud. Biometric authentication via fingerprint scanning, facial recognition and voice recognition can more accurately verify the borrower’s identity, minimizing the possibility of identity theft.
AI platforms can easily cross-reference information with different databases to detect forgeries or discrepancies that may indicate fraud. Advanced systems find anomalies or outliers that may be fraudulent activity, such as inconsistencies in property appraisal. AI systems may give borrowers who would not have received high grades from traditional credit scoring methods better terms.
AI-powered product and pricing engines analyze the borrower profiles in real time and quickly recommend loans tailored to their needs. Product and pricing engines use dynamic pricing to analyze data, determine risk and provide personalized loan recommendations. Since the engine uses dynamic pricing, lending institutions base their recommendations on up-to-date market conditions, interest rates, the borrower’s risk profile and other factors, allowing for more competitive loan offers.
This feature provides predictive analytics, enabling more informed lender decisions. Using a product and pricing engine benefits the borrower by getting faster approvals, better loan options and competitive rates. The lender benefits from improved efficiency, reduced costs and increased customer satisfaction.
“The mortgage sector has many of the same concerns about AI technology that other industries must deal with, including privacy, implementation costs, unintended bias and data quality.”
Unbiased data
The mortgage sector has many of the same concerns about AI technology that other industries must deal with, including privacy, implementation costs, unintended bias and data quality. Lending institutions should have robust cybersecurity protocols because they collect personal and financial information from applicants. Although implementing an AI system is costly, prices should decrease as adoption grows.
Since AI algorithms rely on data, ensuring high-quality, unbiased data is crucial to avoid perpetuating existing inequalities or creating new discrimination in lending. Diligence is the key to preventing bias, so we are far from eliminating human oversight.
Lending institutions see the potential of AI-powered underwriting. They understand the positive impact of AI-powered chatbots, streamlined communications, instant income and employment verification, risk assessment, fraud detection, and product and pricing engines. These mortgage companies also know the challenges, such as biased data, privacy issues and cost concerns.
In the coming years, more lenders will adopt AI technology to increase efficiency, reduce human errors, improve customer satisfaction and approve mortgages faster. This should reduce the horror stories people hear about applying for a mortgage. The homebuying experience will be much more efficient because of AI.
Author
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Kuldeep Saxena is a project manager who oversees mortgage and lending projects for Chetu, a global custom software solutions development and support services provider. Saxena, who has been working for more than 10 years at Chetu, has a master’s degree in computer applications and more than 15 years of experience in IT software.