Before big data was tamed by technology, Most Banks relied on
samples from certain data sources and information’s as the usual approach to
understanding customers.
But with big data technology, Banks can increase the process
and analyze data from its full customer dataset. Financial firms are using big
data to understand certain aspects of their customer relationship that they couldn’t
previously get at. In the industry as well as several others, including retail,
but the big challenge is to understand multi-channel customer relationships.
Most Banks can employ a number of quantitative
analysts, but for the big data era they have been consolidated and
restructured, with matrix reporting lines to both the a central analytics group
and to business functions and units. They can also modify their
structure to make big data more effective, which can
enable consumer banking analytics unit, for example, made up of the
quantitative analysts and data scientists, to reports to the head of Consumer
Marketing and Digital Banking.
Even though some of the problems have been amassing the data,
but some have also been in the bank’s organizations of people and technology.
Internet channels didn’t necessarily share information with the call center or
branch personnel, for example, and the technology silos both reflected and
entrenched fiefdoms
Here are a few ways Banks can leverage on big data from
different channels:
1. Fraud Detection and Security
Prevent fraud by leveraging analytics, machine learning, and Big Data technology
to gain a holistic view of customers, identify patterns buried in data, cluster
information, and distinguish fraudulent activity from normal activity.
2. Compliance and Regulatory
Reporting Comply with a key provision of the Dodd-Frank Act that requires big
swap traders to document everything that goes into each swap trade by
implementing a deal monitoring system based on a new generation of Big Data
technology.
3. Customer Segmentation Group
customers into different segments to support sales, promotion, and marketing
campaigns by collecting and analyzing all available data and using Big Data
technology to mine for intelligence from underlying data.
4. Risk Management Support new
regulations and increasing demand for better internal management support by
implementing a central, integrated finance, and risk management data platform
that can quickly and flexibly address new requirements.
5. Personalized Product Offering
Target new product and service offerings to the right customers by implementing
software that supports flexible and integrated processes for understanding
customer buying habits, what channels customers listen to, and who the key
influencers are.
The
sales channels can be made to communicate with each other, so a customer who
starts an application online but doesn’t complete it, could get a follow-up
offer in the mail, or an email to set up an appointment at a physical branch
location.
Also the
use of transaction and propensity models to determine which customers have a
credit card or mortgage that could benefit from refinancing at a competitor and
then makes an offer when the customer contacts the bank through online, call
center or branch channels.
In summary
it is interesting that while a lot of big data talk is about unstructured data
or social media analysis, banks seems to have plenty of work just to understand
the mostly structured data they already have and generate daily.
And there
is a lot that banks can use Big Data for if only they leverage the different opportunities
abound.
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