Financial services organizations are using big data technologies like Hadoop to improve operational efficiencies, detect fraud more accurately, manage risk, and reduce customer churn. Some key use cases illustrated how Hadoop is being used: 1) Fraud detection by flagging anomalous transactions in real time, 2) Customer segmentation analysis by collecting and analyzing customer data to group customers, 3) Customer sentiment analysis by analyzing social media to understand customer satisfaction.

1. Financial Services Use Cases
Financial services organizations around the world are experiencing drastic change. The global financial
crisis of 2008 resulted in the failing of scores of banks, which also impacted incomes, jobs, and wealth.
As a result, financial institutions need to work hard to avoid the repeat of such a crisis.
Additionally, financial sector companies realize that in order to thrive in a market that has changed so
dramatically, they need to be able to improve their operational efficiencies, detect fraud quicker and
more accurately, model and manage their risk, and reduce customer churn. To accomplish this,
financial services firms are turning to big data technologies and Hadoop to reduce risk, analyze fraud
patterns, identify rogue traders, more precisely target their marketing campaigns based on customer
segmentation, and improve customer satisfaction.
Below are a few of the use cases that illustrate how big data and Hadoop are being integrated in the
financial services industry, providing companies with insights into their operations, their customers,
and their markets.
Fraud Detection
Flagging anomalous activities in real time can help prevent potential security attacks or fraud. The
MapR Distribution for Hadoop gives banks the ability to build usage models of “normal” behavior
from histories of consumer behavior, analyze incoming transactions against individual and aggregate
purchasing histories and take appropriate action if the activity falls outside the confidence level of
normal behavior. As more data is ingested, more precise models can be built so the system can more
accurately separate the atypical but legitimate behavior from the suspicious activities.
Customer Segmentation Analysis
Banks can create a more meaningful and effective context for marketing to customers if they can define
distinct categories, or “segments” in which each customer belongs. Often, these segments are defined
based on demographic information, but the more cohesive and useful segments are also defined by
customer behavior. Banks can define better customer segments by using the MapR Distribution for
Hadoop to collect and analyze all of the data that they have about their customers, such as daily
transaction data, interaction data from multiple customer touchpoints (e.g., online, call centers), home
value data, and merchant records. Banks can then analyze these data sets to group customers into one
or more segments based on their needs in terms of banking products and services, and plan their sales,
promotion and marketing campaigns accordingly.
Customer Sentiment Analysis
The growing number of channels through which customers communicate has resulted in banks needing
to understand what their customers are saying about their products and experiences in order to ensure
customer satisfaction. Banks can use the MapR Distribution for Hadoop to analyze comments on social
media or product review sites, enabling them to quickly respond to negative or positive comments.
With this new insight, not only can banks respond to emerging problems in a timely manner but they
can also more effectively connect with their customers and gain a better understanding of the types of
banking products and services that customers find valuable.
Risk Aggregation
Big data techniques can be used to gather and process risk data in order to 1) satisfy risk reporting
requirements, 2) measure financial performance against risk tolerance, and 3) slice and dice financial
reports. The MapR Distribution for Hadoop can benefit risk managers as they can perform on-demand
historical analysis of risk data as well as receive real-time alerts when limits are breached.
Counterparty Risk Analytics
Whenever a firm engages in a business transaction with another party, the risk of doing business with
that party must be priced into the terms of the deal. Since calculating counterparty risk requires more
than computing a formula, firms typically run long and complex “Monte Carlo simulations” to get a
complete picture of risk exposure at many points in time in the future. These simulations require huge
volumes of data, massive parallel compute power, and system reliability to ensure firms can continue
with business operations with no downtime. The MapR Distribution for Hadoop provides the
performance, scalability, reliability, and the easy access and delivery of data to drive the key
components of a counterparty risk analytics system.

2. Financial Services Use Cases
New Products and Services for Consumer Credit Card Holders
Making new products and services available to consumer card holders is an ongoing initiative for
banks. Improved marketing campaigns and ads through effective targeting are required in order to
deliver services to consumers and increase revenue for banks. The MapR Distribution for Hadoop is
used to provide new products and services for consumers in real time at a leading credit card company.
Advanced machine learning and statistical techniques are employed over data that is stored in a highly
available Hadoop cluster. MapR gives the credit card company the ability to use machine learning
techniques for multiple purposes, including fraud detection and recommendations.
Credit Risk Assessment
Due to the global financial crisis, there are now much more stringent rules for determining whether or
not to give a customer a loan, so banks need more accurate ways to determine a person’s credit risk. A
number of quantitative indicators are used for credit risk assessment and credit scoring. The MapR
Distribution for Hadoop enables banks to pull in customer data on everything from deposit information
to customer service emails to credit card purchase history in order to gain a holistic view of their
customers. With the MapR Distribution for Hadoop, financial institutions now have the tools they need
to construct an in-depth view of their customers so they can properly provide accurate credit scoring
and analysis.
360-Degree Customer Service
To offer optimal customer service, financial services institutions need to analyze unstructured data
about their customers (social media profiles, emails, calls, complaint logs, discussion forums, website
interactions). By analyzing this data, firms gain a much deeper understanding of their customers’
needs, and can respond accordingly with the right products and services. Using the MapR Distribution
for Hadoop financial institutions are able to consistently optimize each customer's experience when
those customers interact with the firm.