specific concepts or ideas. If they begin to require electronic
submission of financial projection spreadsheets, they can test
scenarios in the financial projections by varying the numbers
in a forecast. With a few keystrokes, they can answer questions
like: What happens if the price of your product drops by 10
percent? Does it break your business model? Similarly, they can
click on hyperlinks in a document to verify sources versus having to move from paper to computer to investigate the veracity
of the data in an application. This kind of thorough and intense
examination is only possible when working with documents in
an electronic medium.
How to approach the changing environment
Petitioners can employ a number of strategies to cast their
application in the best light:
Beyond allowing an individual adjudicator to probe
more incisively into any petition, once EB-5 submissions go
fully electronic, the evaluation can become more automated,
looking for patterns that correlate with success or with fraud.
The documents can be parsed according to a pre-determined
algorithm. They can perform standardized keyword searches or
institute a uniform scoring system. While a human element will
be involved, some level of automation in the approval process
is predictable. Loan approvals in the banking industry are a
likely analogy—a loan officer may have some discretion, but
much of the process is determined by a set of variables logged
into an electronic system. Just like applying for a mortgage or
credit card, in an EB-5 application this process could look like
harvesting a few data points from a submission (i.e. industry,
deal size, projected income, etc.), and then crunching them to
spit out an immediate denial, approval, or RFE.
Stick to business fundamentals. A good business is a good
business—period. Build your business plan around a venture
that is designed to yield a return, regardless of the EB-5 aspect. You want a credible plan into which a rational investor
would buy. This is the standard set by the Matter of Ho. If you
apply this principle, when USCIS, or their computer, reads
your business plan it will make sense to them.
At the highest level, electronic submission allows a “big data”
approach to be applied to these petitions, allowing USCIS
computers to analyze relationships and predict outcomes. In the
same way that online advertisers (like Amazon) can predict the
preferences of an individual user (i.e. maybe you’ll also like…),
USCIS can begin to cross-reference many pieces of data in an
effort to predict the behavior or success of its petitioners. For
example, the agency can begin to pull data from other sources
(i.e. LinkedIn or Facebook) to garner more information on, and
perhaps better assess, their applicants. Such far-reaching and automated analysis might allow USCIS to draw some conclusions
more accurately, while leaving them to draw inaccurate conclusions in situations that require human judgment and nuance.
There is one level of scrutiny when USCIS evaluates an application through the lens of what is submitted in the petition;
there is an exponentially more intense level of scrutiny if they
should evaluate the submission through the lens of all the
information that is available to them on applicants, investors,
and prospective transactions (USCIS sits in the Department
of Homeland Security, after all). This kind of predictive
modeling is not a stretch. It is already being applied in many
contexts—business, intelligence agencies, manufacturing, and
science—and would be natural to apply to EB-5 submissions.
Make sure the key theses are clear. Every sustainable business has a basic underlying premise (i.e. building an assisted
living center in a place with aging demographics and little
competition). This idea must be readily apparent everywhere
in your business plan, projections and economic forecast.
Keep it crisp and obvious, and then support the key ideas
with the numbers. This approach allows adjudicators to easily
synthesize the information in your application.
Annotate and reference all research carefully. Every assumption in the business pla