1. Binary predictions answer questions that elicit a yes or no response. For
example, “Does the order contain data that the artificial intelligence applica-
tion has previously flagged as fraudulent?” Or, “Based upon data that comes
from an AI-enabled recommendation engine, will a customer be likely to buy
an ‘up sell’ product?”
We leverage more applications for these types of predictions than for the other
types, because the responses are far less complex: yes or no. Thus, these types
of artificial intelligence use cases often find themselves in typical business
processes, such as order processing, credit check systems and recommenda-
tion engines used to suggest videos, music or other products to users based
upo n gathered data and learned responses. We found that finances and man-
ufacturing verticals tend to benefit most from this aspect of artificial intelli-
gence (Figure 2).
2. Category predictions involve looking at a data set and then, based upon
learned information, placing that information into a particular category. This
is useful when very different types of data are being analyzed, and categories
need to be applied so the data can be better understood and processed.
For instance, insurance companies place different instances of claims in spe-
cific categories, based upon what they’ve learned over the years. An example
would be to define the likely cause of an accident, even if such information is
not a part of the data. Thus, the AI system can make assignments such as “alco-
hol likely involved,” “likely fraudulent,” or “likely weather related,” based upon
past learning, such as the time of day that the accident occurred, as well as
location, the type of damage done, age of driver, etc.
Category predictions have many different types of applications, such as when
we need to place additional meaning around data, but direct correlation data is
not in the existing database. Finance, manufacturing and retail are all verticals
that can use this category prediction type of technology.
We found that the finance and healthcare verticals can especially benefit from
this aspect of artificial intelligence (Figure 2). For instance, a financial user
may need to classify transactions into categories, such as “likely fraudulent” or
“likely non-compliant.” Or, hospitals may need to categorize test results that
come back from the lab.
3. Value predictions are more complex, but also more insightful. They actually
tell you quantitatively about likely outcomes from data culled through using
learning models to find patterns.
Let’s say we want to find out how many units of a product are likely to sell in the
next month. This can be good information to know. It can permit tighter man-
ufacturing planning. It can tell you if additional revenue needs to be generated
to meet quarterly objectives. It can even reduce the travel costs of sales people
following up on leads.
We found that all verticals can benefit from this aspect of artificial intelligence
(see Figure 2), but especially manufacturing, for production optimization, and
government, for defense operations such as threat assessments.
SUMMER 2017 | THE DOPPLER | 39