characteristics, businesses can track
patterns between loan parties over
both time and geography to more effectively target forensic reviews. As red
flags and items for concern are either
confirmed or cleared, the models are
refined and accuracy increases.
Volume forecasting models use historical data to predict underwriting volume
in the short-term (daily and weekly) and
long-term (monthly and even quarterly).
When built on experiential data and predictive macroeconomic variables, these
models can drive multiple approaches
to maximizing staffing, which is critical
for ensuring fast turn times and processing rates.
These approaches can have daily,
weekly and monthly impacts and opportunities:
n Daily. Volume forecasting allows
managers to anticipate periods of
peak volume, allowing cycle times
that customers desire to drive staffing,
rather than having staffing drive the
turn times that customers experience.
If managers can anticipate a volume
surge on Wednesday and a drop
on Friday, schedules can be aligned
n Weekly. Managers can communicate volume and staffing needs
with their teams weeks in advance to
ensure adequate coverage and maintain a quality customer experience.
By anticipating surges far in advance,
managers can secure overtime com-
mitments before personal schedules
are set and schedule meetings, train-
ing, etc., so as to not conflict with
peak volume periods.
n Monthly. By anticipating volume
trends before they occur, managers
can address staffing gaps ahead of
time. Waiting until the volume is real-
ized can leave managers competing
for available resources, thus increasing
costs. Being ahead of the market cre-
ates a more ideal hiring environment.
Volume forecasting allows managers
to be proactive and forward-thinking
when managing staffing. Not only does
this minimize stress for both managers
and employees, it also helps underwriting
teams to consistently stick to offered
turn times and improve customer
The third model type, rules-based modeling, uses decision trees to create workflow waterfalls. This means the model
can be used to route specific types of files
— such as files needing a particular type
of review — to specific underwriters.
This type of modeling is attractive
because of its simplicity. Rules-based
modeling does not require a large
information technology budget, so it
is an ideal tool for smaller companies
seeking increased efficiency. Despite
their simplicity, rules-based models can
introduce significant benefits when
combined with automated data collection and workflow.
Specialization at a granular level can
help reduce training time and allow for
quicker utilization of resources. A prime
example of this is the processing experience on a loan from a single salaried
borrower compared to a loan with multiple borrowers who are self-employed.
The ability to quickly route each of these
files to a separate underwriter who has
experience with those particular loan parameters improves efficiency all around.
As evaluations of loan data and documents against historical experience
and anticipated outcomes becomes
more granular and complex, the opportunities for segmentation grow.
n n n
Underwriting technology is critical to
delivering a quality product and a quality customer experience in the mortgage industry. And happy customers
are more likely to return or give referrals to a mortgage company’s originators in the future.
Ultimately, predictive analytics’ greatest contributions to the underwriting
process are increased insight, greater efficiency and improved accuracy.
Through various models, predictive analytics offer opportunities to improve the
underwriting process by automating
the science and allowing underwriters
themselves to focus on the art. n
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