Tell us a little about yourself, outside of the information that is publicly available and online.
Ah, that’s a tricky one.
Well, it may be interesting to note that before I started my career in finance, I had explored the option of doing an advanced degree at ISI (Kolkatta). Eventually, I opted to pursue finance instead, but with my recent shift in focus to information, analytics and insights, I feel like I’ve come back full circle!
In the initial stages of my career, I focused on traditional finance. However, for the past 15 years, I have worked on a wide range of consumer-oriented roles in business management. Interestingly, both have straddled the banking and retail sectors. To work in these sectors has been an incredible learning experience for me. It has given me the opportunity to gain remarkable insights into the mind of both, customers and retailers while remaining focused on the business and profit dynamics of banking.
Most recently, I’ve been exploring how to leverage information and analytics to drive business performance. This curiosity was heightened due to my learnings from the global financial crisis. I think the chaos it left in its wake could probably have been reduced, to some extent, with predictive analytics.
As the Executive Vice President of CitiBank, one of the largest banks in the world, you watched the world endure and recover from the worst financial crisis since the 1930s. What were your key learnings?
During the financial crisis, I was asked to manage high-risk global accounts for Citi Bank. At the time, the approach we adopted was largely focused on collections and repayments. However, by the time a customer showed evidence of risk, it was often too late to take action.
Hence, the question became – how could we change our approach to identify and manage high-risk accounts better? We did try to use predictive analytics to determine, which customers demanded immediate action and, which ones could be tended to later. But we soon realized that business processes and models were just not calibrated effectively enough at the time for predictive analytics.
Post the financial crisis, the efficacy of financial models was thoroughly scrutinized for what techniques and information sets might be applicable. Unanimously, the conclusion arrived at suggested that there was enormous opportunity for banks to leverage data and improve their performance substantially.
If you compare banks to companies like Google, it’s evident that banks are still at the nascent stage of the digital and data revolution. Banks have very complex embedded business processes and business models. It is, therefore, a significant challenge for banks to migrate to a new state.
I view this challenge as a significant opportunity since it has provided a clear rationale for bringing solutions to the financial services industry across their needs – building scalable(and low cost) customer acquistions, moving towards real-time processes, laying out a framework for becoming information centric and similar activities.
Can you tell us about your role at Crayon Data? What excites you most about your work at Crayon Data?
To be honest, my role at Crayon evolved quite dramatically!
I was having a casual cup of coffee with Srikant, discussing the work Crayon had been doing in Asia.
Srikant wanted to explore the opportunities in the U.S market for Crayon. The U.S market is unique in that companies in the U.S gain credibility only by actually working in the U.S.
And before I knew it, we were enthusiastically chalking out a plan for Crayon to penetrate one of the most competitive markets in the world.
I’ve sat through a number of Crayon’s business meetings and presentations to clients. In all of them, I’ve noticed that there has not been a single client interaction at the end of which the client did not concur with Crayon’s vision, approach, and highly practical recommendations.
What excites me most about Crayon is that I’m proud to say that I have partnered with a credible organization whose product clearly meets the needs of the modern consumer.
What is your view on how the financial services sector & insurance space in the US is changing with the advent of big data analytics?
Right now, banks are being confronted with the realization that the boundaries of what is possible and, in tandem, customer expectations are shifting dramatically. Their business models are no longer as successful as they were in the past.
Banks are only now starting to lay the foundation for a completely new business model. They are shifting their focus to consumers, trying to figure out how to get new customers, deepen relationships and meet demanding customer expectations. They are making efforts to go digital and even employ big data analytics, but the practical state of this migration, on a scale of 1 to 100, is still in the teens.
Accenture estimates that competition from non-banks could erode one-third of traditional bank revenues by 2020. Companies like Paypal, Apple Pay, and Google Wallet. How can banks respond to these threats?
Yes, these companies are stealing away a decent chunk of revenue from banks.
But let’s not forget, there are multiple drivers of banking revenue – lending, transactions, and savings. And neither Apple nor Google or Pay pal are interested in lending or savings because it is heavily regulated and extremely complex.
Banks need to focus more on how to drive market share in traditional asset side lending and think less about external competitors like Google, because the core lending business isn’t going anywhere in the foreseeable future.
With specific regard to Google, Apple, and Paypal, they have caused two problems for banks:
- Banks lose a portion of a revenue stream – revenue they receive from transactions done by their customers – that is high volume and very low risk;
- Banks now have one more platform on which they need to compete for shelf space and allocate scarce marketing resources.
Allow me to explain. For a bank, it’s not enough to have an Apple iOS or Mac application. Since all banks are on the Apple ecosystem, each bank has to figure out how to make their product stand out for a customer to, say, use their credit card.
According to a Transunion study, 8 million credit card users are “inactive”. How can banks get their customers to use credit cards?
Firstly, it’s more than 8 million. It’s probably, multiples of that.
For example, when banks acquire a customer in the credit card space, they fall into one of the 3 broad buckets:
- Customers that use their credit card actively for sales transactions.
- Customers that take a credit card because of low-interest Let’s say a customer gets 0% interest rate for 18 months. There are many customers who will just use the credit card for those 18 months and then become inactive. This set of customers is extremely unprofitable for banks because they spend lots of money to acquire customers, who eventually become inactive.
- Customers who take credit cards but never use them. These customers are completely inactive.
Customers need to be approached differently, depending upon which of the above buckets they belong to. A long standing customer who got tired of a product, for instance, can be enticed to migrate back to the bank with a new product.
Hence, when we talk about inactive customers, identification of the source of that inactivity is an extremely important metric to create an approach for that customer.
43 percent of U.S. customers believe their primary bank does not understand their needs; 31 percent feel their bank is not helping them reach their primary financial goals. Why aren’t banks able to understand their customers?
Because understanding every individual customer has never been a part of a banks’ business model. The top five to six credit card players hold a ninety percent share of the credit card industry in the U.S. These players operate on a very large scale. Citi and JP Morgan each have active credit customers (customers who get a statement every month) at well over sixty million. Shifting from an “actuarial” segmentation structure to a one-to-one relationship is an extremely complex endeavor. This is a problem for all the main banks.
Banks sit on troves of big data and, according to Tresata, only 1 % of this data is used for analytics. How can banks make better use of data, to understand their customers better?
People say banks don’t use transaction data efficiently. This is not only true but is a part of the bigger problem!
For them, using data analytics for better customer satisfaction and deeper engagement is like developing a muscle that’s never been completely used. It requires intense focus, discipline, and persistence.
Further, transaction data provides only limited insight into the mind of a customer. It would be an enormous mistake for banks to leverage only their internal data. They now have to rethink not only their approach towards their own data, but also account for the existence of massive and growing pools of external data.
P.S. Vik Atal will be writing a series of articles about banking tech at the beginning of every month. So stay tuned!