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“The Case for Underwriting Technology,”
“Opening the Box on DU 10.0,”
“Navigating Red Flags,”
Underwriting has traditionally necessitated a fine balance between “art” and “sci- ence,” requiring insights from historical trendsas wellasassessmentsofindividual
circumstances. As underwriting departments and companies seek further efficiency and process enhancements, some fear this balance could be threatened by
the increased reliance on technology in the underwriting process.
One change causing this fear is the increased confidence in predictive analytics, which leverages the
use of big data to make smarter and quicker decisions.
These decisions are offering borrowers, originators
and underwriters process efficiencies, decreased risk
and better customer experience.
Without a doubt, however, predictive analytics are
here to stay. Research performed by LIMRA, the Life
Insurance and Market Research Association, states that
“nearly nine in 10 financial services companies have or
are exploring the use of big data analytics to streamline automated underwriting processes.” With this in
mind, the question becomes whether or not predictive
analytics is truly a threat to the under writing balance.
The use of predictive analytics does not reduce the
qualification elements reviewed or eliminate components of the under writing process, but instead increases the efficiency of how those elements are reviewed.
Rather than simply automating the review of submitted qualification documents, predictive analytics
streamlines the process to maximize efficiency and
This is a critical distinction. The adoption of predictive analytics is not intended to cut corners in the evaluation process. Instead its aim is to drive efficiencies
and allow companies to be smarter about risk.
There are two benefits of predictive analytics:
process automation and cognitive insights. Process
automation streamlines underwriting by allowing the
system to handle pieces of the process where humans
are not needed. Ingesting information such as borrower
reports or asset statements from loan files, for example,
can turn this info into data and run it against a set of
rules designed to test acceptability. The end user is then
presented only with a decision as well as anomalies or
discrepancies that need to be addressed.
Cognitive insight drives efficiency and accuracy by
offering information on more complex portions of the
process to help in the decisionmaking process, but ulti-
mately leaves those decisions to the human users. Mod-
els that run during the origination process and provide
granular visibility into specific credit or manufacturing
risks for given loans, for example, can inform under-
writers about items needed for their evaluation.
Further, cognitive insight equips and teaches human
users, making users faster and smarter.
The addition of predictive analytics to the underwriting process is not transformational, but incremental. It allows underwriters to focus on the subjective
items that require human judgment and intuition,
while allowing the system to handle administrative
items that would otherwise reduce human efficiency.
Predictive analytics offers multiple ways to improve
underwriting, including through loan-classification
modeling, which provides valuable borrower insights;
volume forecasting models, which estimate underwriting capacity; and rules-based modeling, which
creates waterfalls in workflows. Let’s look at each of
these in turn.
Loan-classification modeling uses historical data,
loan characteristics and market variables to classify
loans based on specified characteristics, such as risk.
This classification gives underwriters insights on how
deeply they want to review a loan.
This model can be used to drive efficiencies and im-
prove the customer experience. These benefits can be
further increased by introducing process automation
through cross identification of documents and data
within automated classification and extraction software.
This model can help accurately predict the probability of
outcomes, increase efficiency by directing underwriter
focus and identify indications of misrepresentation.
Accurately projecting the probability of expected
outcomes early in the process can help underwriters
set more accurate expectations for timelines within
the process. The origination process can vary signifi-
cantly, depending on the form of both data and docu-
ments — which are often known early in the process.
Using these data points to estimate likely outcomes
— and updating outcomes as information changes —
can help reduce unnecessary wait times.
Even without fully delegating the decisionmaking to
a predictive model, predictive analytics can still intro-
duce efficiency into the process by driving the under-
writer’s areas of focus. If the likelihood of an outcome is
within an organization’s risk parameters, for example,
the model could be leveraged to point the underwriter
toward only those portions requiring analysis.
Similarly, specific loan characteristics or documenta-
tion patterns could select loans for a heightened, but
still targeted, review at the beginning of the process,
rather than the end. Targeted, concurrent review pro-
cesses can shorten the overall processing time while
reducing the risk of a manufacturing defect.
Similar modeling techniques also can be used to
identify highly predictive indicators of misrepresentations. By analyzing large amounts of data and document
Brian Kucab is director of underwriting at Genworth Mortgage Insurance. He has held various leadership roles with a
focus on risk assessment and mitigation, as well as process
enhancement. Kucab began his financial services career as a
residential underwriter before joining Genworth in 2007 to
focus on contract-underwriting recourse. In 2009, he moved
into loss mitigation as director of investigations, with responsibility for delinquent loan reviews and forensic underwriting.
Reach Kucab at firstname.lastname@example.org.
Predictive Analytics in Underwriting
Art and science must remain in balance when using technology
By Brian Kucab
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