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Chapter 19: Finance and Big Data
There is definitely no short supply of data – in fact, there is probably an
excess of data available today when accounting for traffic running through
social media, transactions, real-time market feeds, and other places. This
means there are explosive amounts of data available for the financial sector.
The variety of data available and the speed of accessing data have also
grown astoundingly. This can create two scenarios for organizations – they
either harness the data and use it for innovation or stand by in awe at the
massive amounts of data they are presented with. Since businesses are in
business to succeed, they have taken the approach of hiring data scientists
to help them sort through data. A data scientist takes a data set, analyzes it
from all angles, and uses it to make inferences or predictions that can
possibly lead to beneficial discoveries.
How a Data Scientist Works
A data scientist’s goal is to identify fresh data sources for analyzation and
then build predictive models. They may even run simulations of possible
events to see the outcomes. All of these factors help the scientist to see a
possible reality of a situation before implementing new innovations or
discoveries. This way of using data allows organizations to foresee trouble
and prepare for it before it becomes uncontrollable and may even show
future opportunities as events play out in the real world.
Many data scientists use software such as NoSQL, Apache™ Storm, and
Hadoop® to sort through and identify non-traditional data like geo-
locations or sentiment data, and then correlate that data with traditional
data, such as trade data.
Data scientists also want to ensure the future of their work, so they take
precautions to ensure there is an available backlog of relevant data for
future reference and find a way to store it cost effectively and safely. Their
expertise and ease in storing data are further enhanced by the development
of technology-based storage “facilities” – for example, cloud-based data
storage. There are also tools of analysis that are quite sophisticated for their
cost-effectiveness, some even being offered for free online as open-source
tools. Data scientists have a wealth of financial tools at their disposal which
are being used to improve and transform how business is conducted.
Understanding More Than Numbers
It might seem that a data scientist is concerned with concrete numbers and
figures, solidly objective data. However, there are services and tools
available that help them analyze people’s sentiments – also known as
opinion mining. A few examples are Think Big Analytics, MarketPsy
Capital, and MarketPsych Data. These firms and programs analyze text,
language processing, and computational linguistics to consolidate the
information into usable material to improve business.
Applying Sentiment Analysis
Sentiment analyzing firms build and use algorithms to compile relevant
data from the online marketplace, for instance, from Twitter feeds. These
feeds provide a massive amount of data when concerned with specific
impactful events such as disastrous weather or terrorist attacks. These feeds
can also be mined by organizations to find trends when monitoring new
products or services or responding to widespread issues that might affect
the image of their brand overall.
For call centers, sentiment analysis can examine recorded phone calls to
find ways to reduce customer turnover and recommend ways to improve
customer retention. Many of today’s businesses are customer-focused so
having a service to analyze data about how or what customers feel toward a
brand is a key factor to the success of the business. The demand for this is
so great that companies have emerged that focus on providing this kind of
service – gathering data, identifying sentiment indicators, and selling their
findings to retail establishments.
Risk Evaluation and the Data Scientist
Data scientists have found ways to use the variety, frequency, and amount
of data available in the online marketplace to enable finance companies to
offer credit online with very little risk. Sometimes investors won’t or don’t
access credit because of the lack of a way to give them a credit rating.
However, it is essential for lenders and financiers to be able to measure the
risk when considering handing out credit.
Internet finance companies have emerged thanks to big data and the
scientists that analyze the data, developing ways of managing risk to be able
to approve online loans. A good example of online lending enabled by big
data analysis is Alibaba AliLoan, an automated online bank that offers
small and flexible loans to online entrepreneurs. Recipients are typically
creative individuals with no collateral, which makes securing loans from
traditional banks nearly impossible.
Reduced Online Lending Risk
We’ll continue to focus on more detail about AliLoan and how they’re able
to manage risk through an online forum. Alibaba monitors e-commerce and
payment platforms to understand customer behavior and their financial
strength. They will analyze a customer’s ratings, transactions, shipping
records, as well as other information, and can generate a loan cap for the
customer and the associated level of risk. Alibaba also uses external third-
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