SSSuman Singh: Fiserv wants to address the analytical needs of Financial Institutions (FIs) based on the type of FI (credit union, community bank etc.), their unique needs and the most economical way to deliver analytical insights to them. For example, we want to deliver analytics as a data service to budget constrained small FIs versus mid to large banks where it is a more iterative & advisory type of analytics offering. Also remember, irrespective of size, customers don’t buy predictive models, they buy solutions powered by analytics to derive profits by either increasing revenue or optimizing expense.
While the competitive dynamics for FI’s could be different by segment, the fundamentals stay the same, which is grow revenue, increase efficiency, reduce costs and streamline operations. Our one point agenda is to provide cutting edge analytical products, solutions and services, which improve end-user understanding, drive revenue from interest and fee based sources, reduce fraud, encourage loyalty and derive insightful meanings into customer transactional behavior which makes banks profitable.
AIM: Please brief us about some business solutions you work on and how you derive value out of it.
SS: Our spectrum of analytical solutions is very wide. We support our internal customers along with providing consulting services to the banking industry across the value chain (retail banks, cards issuers and acquirers, business banking etc.) We carry out two types of engagement. For our internal customers, as an example, we focus on ways analytics can derive insights on how our B2B customers perceive us, which in turn helps us improve the quality of our service offering to them. For external customers, we sell our services based on the business issues faced by the bank.
Based on the data we capture in our core transaction system, we build various predictive models to predict the next likely customer, propensity to buy bank’s products, customer value & profitability (current and future), attrition and customer segmentation based on machine learning techniques. We also provide insights and recommendations to FIs on how to maximize their interest income and optimize cost efficiency. For the consulting engagements, we understand the business problem directly from the client, following which we design an analytical framework and provide an end to end solution.
AIM: Please brief us about the size of your analytics division and what is hierarchal alignment, both depth and breadth.
SS: We have a dedicated team of roughly 25 professionals comprising data scientists, domain consultants and data analysts aligned with various functions like Analytics Advisory Services, Marketing Analytics, Risk Analytics, Card Analytics, etc. The team members have diverse levels of experience and expertise in different roles like statisticians, MBAs and engineers, who collectively work together to provide solutions to our customers.
AIM: Would you like to share any example of an Insight that generated a huge positive impact for your clients?
SS: Recently we completed a project for our banking client and measured the impact. Our predictive model for signing loan application generated 100%+ ROI through better targeting of loan prospect customers. We have also created unique customer segmentation in the cards and core retail banking space which allows our FI customers to develop differentiated service and channel strategies to their customers.
As another example, we recently analyzed the transaction behavior of millions of merchants of a large merchant acquirer. Through data driven insights, we provided recommendations to optimize their pricing policies. We are currently testing the recommendations in market studies.
AIM:Is the information being gained by analytics impacting boardroom decisions or business strategies at all? If so how?
SS: Gaining from my experience at Fiserv, I realized that by mining millions of rows of transactional data and using sophisticated statistical methods we are able to provide tangible insights and identify the stem of core issues like revenue leakage from fee based products, product bundle gaps based on customer segmentation, channel cost optimization issues etc.
AIM: Do you think it’s possible to become too married to the data that comes out of analytics? Where do you draw the line?
SS: This is a great question. Fiserv has a strong expertise in managing large data pools and that in fact is our USP. There are times when we have to dig into millions of transactional data to come up with the right solution/insight. It is easy to get lost in large amount of data, but that is where we need to draw a line and focus on relevant data from that vast data pool to solve the problem at hand.
AIM: What are the most significant challenges you face being in the forefront of analytics space?
SS: Surpassing client expectations on deliverable and time is a huge challenge. When an organization plans to go analytical, there is a strategic shift to generate additional revenue. In data science our approach is SOVAR (Specify, Observe, Visualize, Analyze (the data) & Predict and Recommend).
AIM: What do you suggest to new graduates aspiring to get into analytics space?
SS: If you want to develop a career in analytics, then you need to be focused, think smarter, and assess how you can make the business more profitable by adding value to the customer. You need to be a management consultant using the power of data analytics. It’s a combination of math, intuition and a lot of common sense!
AIM: What kind of knowledge worker do you recruit and what is the selection methodology? What skill sets do you look at while recruiting in analytics?
SS: We employ resources with experience levels varying from 2 to 8 years, who have experience in building predictive models and churning insights from data, with added domain knowledge in banking. Our selection methodology is very simple. We recruit Masters or PhDs in Statistics for the role of a statistician. For positions like Data Miners and Business Analysts, we recruit Engineering graduates and MBAs. We are planning to hire quite a few fresh statisticians and engineers in January 2013.
AIM: How do you see Analytics evolving today in the industry as a whole? What are the most important contemporary trends that you see emerging in the Analytics space across the globe?
SS: Analytics has become the need of the hour for successful businesses to seize right opportunities. It serves like a Mariner’s Compass that provides direction in a world full of data and customers. The industry has woken up to the world of data mining and analytics and finding ways to maximize profit out of it. Big organizations are catching up with cloud based analytical products, which is an emerging trend. Banks are also grappling and trying to make sense of the vast amount of unstructured Web 2.0 data that impacts their business.