Banking on Big Data analytics5 min read . Updated: 23 Jul 2014, 11:51 PM IST
Banks are not new to this tool, but today they are using it to drive revenue, get valuable insights on customers
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.
“The value of analytics is very easy to determine—we use it as a metric to determine the number of instances where we have been able to prevent a fraud. Our NPA (non-performing asset) index is a true measure," he said.
In the June quarter of 2014, NPAs for HDFC Bank stood at 1.10%—one of the lowest among the top 10 Indian banks. It was also an improvement from five years ago when NPAs stood at 2.05%.
To explain the value of analytics, Mittal uses an instance where the technology helped improve revenue productivity.
“We put a case on the table that we’ll run a production for 90 days and we’ll target ‘X’ number of customers in a control group using non-event- based marketing and in a test group using event-based marketing. We took 100 customers in both test and control. We had a 30% target and we got a 45% uplift in cross-sell using event-based marketing analytics and we generated ₹ 17 crore of additional revenue vis-à-vis the control group," said Mittal
Similarly at The Ratnakar Bank Ltd, the use of analytics was one of the several new technology bets that the bank made over the last decade to stay relevant in the face of increased competition.
One of the key banking products that The Ratnakar Bank uses is Infosys Ltd’s core banking solution Finacle.
“We decided to take a non-traditional approach by building our (analytics) solution on an open source platform," said Sanjay Sharma, head of technology, innovation and customer fulfilment at The Ratnakar Bank. “It obviously allows you to understand your customers better and gain unique insights that give you an edge over competition."
Sharma declined to disclose revenue productivity figures, but said that the bank had invested about ₹ 1.5 crore in implementing analytics so far and would invest more over the next few years.
The Ratnakar Bank is one of the more recent banking customers of analytics tools, having implemented them in 2013 and buys analytics software from an Indian start-up, Pragmatix Services Ltd.
“It’s (analytics) definitely a huge opportunity for banks," said Bhavish Sood, research director at Gartner India. “There are some banks that have been able to leverage analytics in a better way than others, and that reflects how well these banks are doing today. The most successful banks are the ones that have embraced and leveraged technology better than the rest."
According to Gartner, Big Data will drive $232 billion in IT spending through 2016. Global IT spending currently stands at well over $3 trillion.
This is the second in a seven-part series.