Page 140 - Building Big Data Applications

138 Building Big Data Applications

insightful exploration. Users gain 360-degree views on what is happening, why it is
happening, what to do next, and who should be involved in the process.
These modern data applications make big data and data science ubiquitous. Rather
than back-shelf tools for the occasional suspicious transaction or period of market
volatility, these applications can help financial firms incorporate data into every decision
they make. They can automate data mining and predictive modeling for daily use,
weaving advanced statistical analysis, machine learning, and artificial intelligence into
the bank’s day-to-day operations.
A strategic approach to industrializing analytics in a banking organization can add
massive value and competitive differentiation in five distinct categories:

1. Exponentially improve existing business processes. eg., Risk data aggregation and
measurement, financial compliance, fraud detection
2. Help create new business models and go to market strategiesdby monetizing mul-
tiple data sourcesdboth internal and external
3. Vastly improve customer satisfaction by generating better insights across the
customer journey
4. Increase security while expanding access to relevant data throughout the enterprise
to knowledge workers
5. Help drive end-to-end digitization

Predicting customer churn

Let us apply the learnings discussed to an application for predicting customer churn,
which is a big drain on resources and finances in banks. The cost of acquiring a customer
is higher than the cost of retaining a customer and all banks try to protect themselves
against an increase in the churn rate, using customer behavior analysis providing an
early indicator for banks. In order for the customer behavior to work effectively, we need
to get a 360-degree global view of each customer and their interactions on different
exchange channels such as banking visits, customer service calls, web transactions, or
mobile banking. The transactional behavior will provide warning signs, such as reducing
transactions or cancellation of automatic payments. However, increasing the volume,
variety, and velocity of the data needing to be exploited has made it nearly impossible to
store, analyze, and retrieve useful information through traditional data management
technologies.
Big data infrastructure has been designed and built to address these challenges by
solving data management problems by storing, analyzing, and retrieving the massive
volume and variety of structured and unstructured data while scaling with extreme
flexibility and elasticity as data increases. The platform and its application will provide
banks benefit from real-time interactions with their customers.
How does this work from an application perspective? Banking applications have all
focused on customer and their journey since banking started as an enterprise. We have