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McClatchy-Tribune  07/24/2014 3:50 AM ET
Banking on Big Data analytics [Mint, New Delhi :: ]

July 24--BANGALORE -- The use of Big Data analytics in the banking and financial services industry is not a new phenomenon.

One of the first instances of the use of analytics can be traced back to the early 2000s when HDFC Bank Ltd, now the country's second largest private sector lender, put in place a data warehouse and started investing in technology that would help it make sense of the massive troves of unstructured data captured by its information technology (IT) systems.

What is new is how lenders such as ICICI Bank Ltd and HDFC Bank are looking at Big Data analytics as a tool to generate more revenue, as they get valuable insights on customers and markets.

Although none of the banks interviewed for this story spoke about how the use of analytics had helped boost their revenue, chief information officers and IT executives of these banks said the return on investment was worth "millions of dollars."

Big Data refers to massive amounts of data captured by IT systems that are too big and complex to be analyzed and processed using conventional software. Using analytics, companies across the world attempt to get insights into customer behaviour and also, in certain cases, solve business problems.

"Back in 2004, we set up a basic backbone for analytics in terms of an enterprise data warehouse in the bank -- we were one of the early ones to set up the data warehouse. And the driver for that was can we track the differentiation to be given to customers based on their relationship value with HDFC Bank," said Munish Mittal, senior executive vice-president of IT at HDFC Bank.

For banks like HDFC Bank, data is generated through multiple channels -- voice call logs, emails, websites, social media and real-time market feeds.

After putting the initial data warehouse in place, HDFC Bank discovered that it needed to integrate the analytics engine with every aspect of the bank's core operations to gain valuable insights on customers that would help improve revenue productivity and lower the risk of being exposed to fraud.

"From 2004 to 2006, we created an enterprise data warehouse so that we had the overall picture of a typical customer in front of us," Mittal said.

With the analytics engine in place, HDFC Bank can track every aspect of a typical customer's financial habits. "For example, we can determine whether the customer has an active account or he's just having a salary credited to his account. Am I the primary bank account for this customer or am I just another account? -- these were the questions before us and the challenge was clear. We wanted to address the question of how to become the primary bank for these customers," explains Mittal.

For example, HDFC Bank started offering Net banking services to customers who were more active in using ATMs or bank branches to carry out financial transactions. "Using analytics, we offered services such as Net banking to customers to make it more convenient, as they didn't have to repeatedly visit branches, didn't have to make a call or go to ATMs multiple times," said Mittal.

The analytics tools also gave the bank insights into personal habits, allowing it to promote offers accordingly. "Can I put my retail assets into it? Does he have a two-wheeler already? Does he have an auto loan already? Does he have a personal loan already? To be able to differentiate the customer and cross-sell relevant offers, we put analytics into play. We wanted to become the one-stop shop for the customer -- he uses our debit card, but does he also use a credit card? At that point, we decided to put an analytics engine on top of our data warehouse and we brought in analytical tools like Saas (software as a service)," Mittal said.

Using analytics, banks are also able to keep track of credit histories of customers and can hand out loans accordingly.

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