Page 136 - Building Big Data Applications

134 Building Big Data Applications

Create modern data applications
Banks, wealth managers, stock exchanges, and investment banks are companies run on
data generated by customer during activity including deposits, payments, balances, in-
vestments, interactions, and third-party data quantifying risk of theft or fraud. Modern
data applications for banking data scientists preferably need to be built internally as they
can be customized on-demand or can be implemented by a purchase from “off-the-
shelf” third parties. The ability to add artificial intelligence and machine learning algo-
rithms have delivered the new applications are powerful and fast enough to detect
previously invisible patterns in massive volumes of real-time data. They also enable
banks to proactively identify risks with models based on petabytes of historical data.
These data science apps comb through the “haystacks” of data to identify subtle “nee-
dles” of fraud or risk not easy to find with manual inspection.

The use cases of analytics and big data applications in
banking today

Understanding your customer interactions.
The customer buying journey requires an extensive optimization of the customer
acquisition and loyalty campaigns; you need greater visibility into the customer buying
journey as it transcends different segments of your business. We need to collect and
integrate analytics across all these activities to improve the journey.
Increase customer acquisition
Increase revenue per customer
Decrease customer acquisition cost