Big Data Use-cases – Banking and Financial Services

Big data has become the latest buzz word in Information Technology world and in the business arena from board room to product development and sales & marketing. There is lot of hype and noise in the industry. Many business leaders are curious about the business use case for Big data that can give them competitive edge and head-start in reaching the finish line before others. Many IT vendors from IBM, SAP, Informatica, Microsoft, Oracle, HP, Cloudera, FOSS vendors are investing and pushing their solutions & offerings into the market place. IBM has invested millions of dollars into Smarter Planet initiative and primarily with BigInsights platform. Since 2005, IBM has spent over $14 billion to acquire twenty-five software companies specializing in data analytics, and today it has over 8,500 analytics consultants. During March of this year. H-P said it will reallocate the $3 billion to $3.5 billion between now and 2014 to Big Data analytics, cloud computing and security infrastructure. Moreover, the U.S. government is investing $200 million in big data projects to help the U.S. jump ahead in the next frontier of computing.

So Big Data is here to stay and change our world. MIT economist Erik Brynjolfsson compares the implications of data analytics to an important invention four centuries ago. Brynjolfsson noted that the invention of the microscope resulted in a “revolution in measurement” that enabled scientists to examine objects at the cellular level, objects that were previously invisible to the naked eye. Using this analogy, data analytics is a key enabler for organizations to see previously undetectable patterns in data in order to better understand risk exposure and to better predict decision outcomes (predictive analytics).

Some of the industry in general like ECommerce, Retail, Banking and Financial has made some capital investment in Big Data in past 2 years while Healthcare, Manufacturing and Utility are gaining traction. Here’s few that I have seen as industry use case that’s being considered as Pilot or Proof of Concept/Value projects.

Big Data use-cases in Banking & Financial Services

1. Fraud Detection:

You may not be surprised that Banks and Credit cards are monitoring your spending habits on real-time basis. One of the large credit card issuing bank has implemented fraud detection system that would disable your card if they see suspicious activity based on your past history with spending patters and trends. In addition to the transaction records for authorization and approvals, banks and credit card companies are collecting lot more information from location, your life style, spending patterns. Credit card companies manage huge volume of data from individual Social Security number and income, account balances and employment details, and credit history and transaction history. All this put together helps credit card companies to fight fraud in real-time. Big Data architecture provides that scalability to analyze the incoming transaction against individual history and approve/decline the transaction and alert the account owner.

2. Customer Segmentation

Customer Segmentation applies in every industry from Banking to Retail to Aviation to Utility and others where they deal with end customer who consume their products and services. In Banking & Financial industry, customer segmentation is a key tool in risk scoring analysis and for sales, promotion and marketing campaigns. In addition to existing information that banks and FIs collect from day to day transactions from customers, they are also buying external data like home values, merchant records from hotels, aviation, retailers, etc. The 360 degree view of customer is still a work in progress and Big Data is enabling filling in the gaps by providing the processing power needed to mine for intelligence from underlying data.

The major objectives of segmentation are:
  • Customized product offering
  • Customized and priority service
  • Improve relationship with profitable customers and cut resources spent on loss making customers
  • Better offering to new customers based on the intelligence gained from the existing customer segment they belong to
  • New product development and bundling as per the customer segment profile

3. Customer Sentiment Analysis

The bank can now respond to negative (or positive) brand perception by focusing its communication strategies on particular Internet sites, countering – or backing up – the most outspoken authors on Twitter, boards and blogs. When a company releases a new product that’s causing problems, analyzing comments in social media sites or product review sites can enable it to quickly remediate.

4. Crowdsourcing

Some of the larger institutions have realized they can use analytics to learn about new lines of business and products, to ask customers what they think, and to get ideas. In a move to expand its utility beyond simply finding better answers to known statistical problems, data-science startup Kaggle is now letting its stable of expert data scientists compete to tell companies how they can improve their businesses using machine learning.

4. Sales and Marketing Campaigns

On the customer experience side, every time you get closer to delighting your customer by showing that you understand what their real needs are, without blindly sending them emails and credit card offers, it makes the customer view their institution as caring about them and understanding what their needs are.

5. Call Center Analysis

For decades, companies have been analyzing call center data for staffing, agent performance, network management. But with big data age, many new interesting software are being implemented today in attempt to take unstructured voice recordings and analyze them for content and sentiment. Banks are applying text and sentiment analysis to this unstructured data, and looking for patterns and trends. Many banks are integrating this call center data with their transactional data warehouse to reduce customer churn, and drive up-sell, cross-sell, customer monitoring alerts and fraud detection.

These are just few of the use cases that I have highlighted here to give a fair idea about how Big Data is being leveraged in this industry. If you have use-case that you are working with, please add it to the comment section.