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fit the unique needs of the servicing industry
are best positioned to disrupt the status quo of
asset decisioning in 2021.
How can AI/machine learning serve as a
"crystal ball" for servicers?
Servicing on its face is simple: there's an
asset, and it needs to be managed. But there are
a host of incremental decisions that could make
consequential impacts on the ultimate outcome.
Machine learning and analytics opens the door
to more rapid and confident decision-making
and can help make sense of an ever changing
economic and mortgage industry landscape.
In the past, servicers needed to base their
decisions on their own historical information
and best practices. Today, they can rely on
machine learning to take in and process massive
amounts of data from a variety of sources—
like historical operational data and leading
industry databases—to produce a new level of
intelligence. Machine learning can streamline
the evaluation process by identifying patterns
that enable it to predict outcomes under various
sets of circumstances; for example, how a
specific property might fare at auction or in the
CWCOT second-chance program, or what
level and cost of repairs a particular property
might require if held for a certain period of
time.
Leveraging technology and AI/machine
learning can make a world of difference for
time-strapped servicers, but it doesn't absolve
them from the decision-making process
altogether. While it's the closest thing we have
to a "crystal ball" in servicing, machines and
technology should always be a complement-to
servicer's decision-making, not a replacement-
for.
"Machine learning and
analytics opens the
door to more rapid
and confident decision-
making and can help
make sense of an ever
changing economic
and mortgage industry
landscape."
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and content focused on their industry.
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