Banking and the Financial Services Industry is a domain
where the volume of data generated and handled is enormous. Each and every
activity of this industry generates a digital footprint backed by data. As the
number of electronic records grows, financial services are actively using big
data analytics to derive business insights, store data, and improve
scalability.
Technology has made the Banks to work in tandem to harness
the data for intelligent decisions. This has prompted many BFSI organizations
to disrupt their analytics landscapes and gather valuable insights from immense
volumes of data assets stored in their legacy systems.
Harnessing Big Data in Banking
Following the Great Recession of 2008 which drastically
affected global banks, big data analytics has otherwise enjoyed decade old
popularity in the financial sector. When banks began to digitize their operational
processes, they needed to ensure different means which were feasible to analyse
technologies like Hadoop and RDBMS (relational database management systems) for
their business gains.
These business gains have been made possible with the
existing data analytics practices that have simplified the monitoring and
evaluation of the vast amounts of customer data which include personal and
security information. With great trust on technology to handle the growing
customer volumes and more transactions, the overall service level offered by
the organizations has also enhanced.
Working with Big Data, banks can now use a customer’s
transactional information to continually track his/her behavior in real-time,
providing the exact type of resources needed at any given moment. This
real-time evaluation boosts the overall performance and profitability of the
banking industry thrusting it to further into a growth cycle.
Banking is an industry which generates data on each step,
and industry experts believe that the amount of data generated each second will
grow 700% by 2020. The financial and banking data will be one of the
cornerstones of this Big Data flood, and being able to process this data
goldmine means gaining a competitive edge over the rest of the financial institutions.
The Four Pillars of Big Data
The big data flows can be described with 3 V’s. That
includes variety, volume and velocity. Here is how these relate to the banks:
• Variety is
the different data types processed. Banks have to deal with huge numbers of
various types of data day in and day out. From transaction details to credit
scores and risk assessment reports, the banks have troves of customer data.
• Volume is
the space that the data will take to store. Giant financial institutions
like the JPMorgan Chase., China Construction Bank Corporation, and BNP Paribas,
etc. generate terabytes of data daily.
• Velocity is the
speed of adding new data to the database. With the volumes that the banks
of today work on, handling 1000+tranactions is not a hypothetical figure.
These 3 V’s are useless if a business does not have the 4’Th
one which corresponds to Value. Value for the banks corresponds to applying the
results of big data analysis real time and to make business decisions.
The banks can make strategies based on these pointers:
• Customer
segmentation based on their profiles
• Cross-selling and
Up-selling based on the customers’ segmentation
• Improvement of
customer service delivery on based on their feedbacks
• Discovering the
spending patterns and making customised offerings
• Risk assessment,
compliance & reporting that aid to fraud management & prevention
• Identifying the
main channels where the customer transacts like credit/debit card payments and
ATM withdrawals.
Banks have several used cases to showcase the different ways
where the data have been harnessed and used for intelligent analysis. This data
opens up new and exciting opportunities for customer service by improving TAT,
and customised service offerings.
Improving Customer Experience
With so many financial institutions in the market, it gets
tough for the customer to decide which bank to transact with. Customer experience,
in this case, becomes a deciding factor. Big data analysis presents with the
customised analysis like claims analysis by
https://conjointly.com/blog/testing-claims-for-consumer-products/ for example,
for each customer, thus improving their services and offerings.
Personalised Marketing
Big Data is used for personalized marketing, targeting
customers on the basis of their individual spends. Analysis of the customer behaviour on social
media through sentiment analysis helps banks create credit risk assessment and
offer customised products to the customer.
Optimized Operations
Big data can be applied to bring immense value to the bank
in the avenues of effective credit management, fraud management, operational
risks assessment, and integrated risk management. Systems that enable with Big
Data can detect fraud signals further analyse them real-time using machine
learning, to accurately predict illegitimate users and/or transactions, thus
raising a caution flag.
Conclusion
The BFSI industry will obtain a better grasp of its needs,
by aligning with the latest technologies like Big Data and the other global
trends both internally into their operations and with customers. This will help
the BFSI industry to provide improved services in a timely manner with optimized
operational costs. Though the implementation of Big Data on a large scale has
just started to evolve in the BFSI industry, the sooner organizations adopt Big
Data practices, the quicker they will be able to unlock the benefits this
technology brings to their business.
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