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Why is AI required for the future of mortgage lending?

share save 171 16 - Why is AI required for the future of mortgage lending? | Chris Grimes | Sep 20, 2021

Why AI is required for the future of mortgage lending - Why is AI required for the future of mortgage lending?

The Mortgage Tsunami!

Mortgage lending in 2020 (and beyond) is set to break previous records. An estimated volume of over $4 trillion of new mortgages will be signed across North America. This increase in mortgage volume is accompanied by massive growth in refinancing, which is estimated to reach $2.4 trillion, more than twice the total of last year.

Considering the sheer volume of mortgage transactions, a vast amount of data is collected on the borrowers with each passing year. Mortgage lenders, no matter how big or small, need to harness the power of this data to make reliable predictions on the borrowers’ future behaviour.

Mortgage lenders rely on data to underwrite their loans, check eligibility, and detect fraud. The problem today is that it takes a significant amount of manual labour to extract data from paperwork. This lengthens the process and makes it costly.

What role can AI play in pivoting the future of the mortgage industry?

One of the main issues with traditional mortgage processing is its delayed response. Despite providing details up-front, a borrower must wait weeks for an approval. Analyzing customer responses and identifying the difficulties faced across different stages will help lenders streamline their application and approval process.

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Roughly 40% of lenders are already using AI in some capacity within their organization. We can expect automated underwriting systems to predict the probability of default for individual borrowers. The detailed review processes for loan approvals can be compressed into hours, facilitating a better decision-making process for lenders and other parties (such as insurance providers).

Future AI programs will utilize data points and indicators outside the mortgage application process (such as social media activity, geo-location, browsing patterns, and other online behaviours) to assist in lending decisions.

Streamlining and improving lending practices

AI helps financial technology (fintech) lenders underwrite loans faster than their traditional counterparts, as revealed in a 2018 study conducted by the Federal Reserve Bank of New York. While most fintech firms don't disclose the exact process, the following is what it may resemble.

Machine learning allows lenders to capture and analyze vast amounts of accurate data and channel it through automated work processes. It also helps lenders identify discrepancies in data, assess loan quality and detect aberrations throughout the loan origination & due diligence process. This allows underwriters to minimize time spent on each file and invest more energy in managing exceptional cases.

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In the case of missing data, an automated system communicates directly with the borrowers, collecting the necessary information. The system may require human input, but as machine learning gets better trained by larger datasets, it will become more accurate. In short, machine learning will automate tasks that were once performed by their human counterparts.

High-value datasets can be "sliced and diced" to obtain critical insights into current operations, design and implement workflow improvements, and eliminate potential problems related to lending practices.

Intelligent capture technology will also allow mortgage lenders to address one of the most critical challenges: high staffing costs. With advanced AI tools, streamlined processes and staff efficiencies can substantially decrease the cost of loans while delivering a more satisfactory borrower experience. In the foreseeable future, sophisticated AI programs will facilitate automation of the decision-making process throughout the loan's lifecycle.

What does this mean for the future of mortgage lending?

A personalized and superior customer experience! In addition to achieving operational efficiency, mortgage lenders will become consumer-friendly.

Several financial institutions (traditional and fintech) are using AI to enhance customer experience. Using the right datasets, it is possible to create an application form that can respond intelligently to clients, adapting to previous answers.

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AI modelling can help predict which potential clients should receive more attention from their marketing team in order to close the sale. This helps boost revenues and brings down the cost per unit. Focusing on stronger opportunities allows for targeted resource allocation and eventually produces greater results at lower costs. The savings achieved through these efforts can be passed on to clients in the form of lower rates, which in turn can help lenders increase their market share.

While AI has made substantial progress in the past decade, current technology is merely scratching the surface of innovation in the mortgage lending industry. The ongoing race to advance the AI-driven mortgage lending process is a big win for fintech, lenders and consumers.

Authored by:

Chris Grimes, CEO & Co-Founder of has over 15 years of experience in the mortgage and lending space. Seeing an opportunity to automate many of the tasks within his company, he realized that he could build a great aggregation tool that leveraged artificial intelligence and provide a full end-to-end lending platform.

Fintech Confidential issue 3 cover 1 - Why is AI required for the future of mortgage lending?

This article appears as a featured article in NCFA's digital magazine, Fintech Confidential. Click to read the latest thought leadership, insights and trends about Fintech in Canada:

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NCFA Jan 2018 resize - Why is AI required for the future of mortgage lending? The National Crowdfunding & Fintech Association (NCFA Canada) is a financial innovation ecosystem that provides education, market intelligence, industry stewardship, networking and funding opportunities and services to thousands of community members and works closely with industry, government, partners and affiliates to create a vibrant and innovative fintech and funding industry in Canada. Decentralized and distributed, NCFA is engaged with global stakeholders and helps incubate projects and investment in fintech, alternative finance, crowdfunding, peer-to-peer finance, payments, digital assets and tokens, blockchain, cryptocurrency, regtech, and insurtech sectors. Join Canada's Fintech & Funding Community today FREE! Or become a contributing member and get perks. For more information, please visit:


share save 171 16 - Why is AI required for the future of mortgage lending?

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