Artificial Intelligence (AI) and Machine Learning (ML) are moving
from bleeding edge technology to an expected competency for
every organization. By 2021, businesses are expected to spend
$57.6 billion on AI and ML and to reap $2.9 trillion in business
value. AI is behind the headline-grabbing applications such as self-
driving cars and virtual personal assistants (Siri, Alexa, etc.), but it's
also providing better performance and cost savings for universal
tasks such as online chat and customer support, product
recommendations, design support, and fraud detection. For enterprises, the decision has become not whether to use ML,
but where to use and how to do it well.
Traditional Solutions Are Missing the Mark
Machine Learning uses analytical and statistical techniques to
uncover patterns in data and thus draw conclusions -- and
recognizing patterns is the basis of intelligence. And therein
lies two limitations: how good is your data and how good are
You can't detect a pattern in data if the pattern isn't there or if
the pattern is very weak. Remember the 3 V's of Big Data:
Volume, Variety, and Velocity. To succeed, your data needs
to consist of millions of records, to cover a variety of cases,
and hopefully to draw from multiple sources. In our
increasingly digitized world, harvesting raw data is much less
of a problem.
Several challenges remain, however. Feature selection: Do I
have the right data? Data integration: How do I bring my data
from multiple sources together into one unified data model?
Analytical performance: With so much data, can I afford the
Traditional solutions often fail due to a lack of variety of
features that have a high correlation to the outcome and
low volume of the training data, resulting in poor accuracy
for ML solutions.
Why TigerGraph, a Native Parallel Graph Database for Powering AI and Machine Learning?
Feature Engineering with Real-Time Deep
Graph databases offer solutions to many of these ML data
challenges. Graphs are built on the idea of connecting and
traversing links, so they are the natural and performant
choice for data integration for ML. Graphs can also enrich the
raw data. In traditional tabular data, each column is one
"feature" that the ML system can use. In a graph, each type
of connection is an additional feature. Moreover, small graph
patterns, such as causal chains, loops, and forks can
themselves be considered features. TigerGraph's native
parallel graph architecture with deep link analytics means it
can process the datasets with terabytes of data, and traverse
millions of connections in a fraction of a second computing
new graph-based features.
Fraudsters get away with billions of dollars of fraud each
year. The measure of an anti-fraud detection and prevention
system is not whether it can catch fraud, but how much.
China Mobile is using TigerGraph to check each of its
hundreds of millions of daily calls in real time to see if it looks
to be from a spammer or phone-based fraudster.
China Mobile gathers over 118 graph features for each of its
hundreds of millions of subscriber phones to feed its ML fraud
detection engine, builds a detection model, and then again
extracts the 118 features for each phone with every call in
real-time. This generates training dataset with billions of new
features, resulting in improved accuracy of the fraud
One of the biggest obstacles to widespread AI adoption in the
government as well as the enterprises such as banks,
insurance, and telecom companies is a lack of transparency
in how the AI system arrived at a particular decision.
Consider a welfare claim that was rejected, as the AI solution
computed a high probability of potential fraud based on the
history of previous claims from the recipient.
recipient deserves to know why his or her claim was rejected
and the government organization needs to make sure that it
wasnâ€™t due to a bias against a specific race, religion, gender
or culture. Consider another example where the AI system
computes and offers a mortgage with a higher rate of interest
or an insurance policy with a higher premium to an applicant.
Again, itâ€™s important for the bank or insurance company to
explain the higher rate of interest for the loan or higher
premium for a policy especially in case of the litigations
related to a race, ethnicity, culture or gender bias. Graph-
based features clearly show how the AI/ML solution arrived at
a particular decision based on the combination of the
computed feature values. TigerGraph also offers
GraphStudio, a powerful GUI for visualizing, exploring and
analyzing relationships to show how the features were
computed and what led to a particular welfare claim being
rejected or a higher mortgage interest rate for an applicant or
a higher premium insurance policy.
TigerGraphâ€™s native parallel graph architecture ensures that
the explainable AI can be rolled out to all the users within the
enterprise as well as external parties such as the welfare
recipients, mortgage and insurance policy applicants with
real-time visualization, exploration, and analytics of