NCFA Fintech Confidential December 2020 (Issue 3)

WHY IS AI
REQUIRED FOR
THE FUTURE OF
MORTGAGE
LENDING?
The Mortgage Tsunami!
Mortgage lending in 2020 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. 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, geolocation,
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. 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
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