Sudip Chakraborty, Principal

Profile
Sudip is a Principal and co-founder of Inductis. At Inductis he has advised Fortune 500 clients for over 7 years in several years including strategic information management, customer data integration, customer data matching, marketing analytics and risk analytics. He is a co-author of a patent-pending algorithm on approximate text matching. He is currently the Head of the Banking and Financial Services practice within the Analytical Consulting division of Inductis.

In his current role, he assists major banks and credit card companies improve profitability by deriving insights from data using a combination of data integration and sophisticated analytics. He has assisted a Fortune 100 Financial Services Company develop a single customer view for small businesses, has advised a Top 10 US bank on acquisition growth strategy for a small business card portfolio and has assisted a Top 3 US bank on improving customer retention for multiple credit-card portfolios.

1) What are the major challenges faced by US financial services companies today?

The overwhelming issue that several banks are facing is the mortgage crisis. Many of the large banks have declared billions of dollars in losses. However, credit card companies have not seen a major impact as yet but they have seen bad debt jumping by almost a third in one year.

2) How can analytics help address these challenges in the short term?

In the short term, analytics can be used to drive key decisions both at a customer level as well as at a portfolio level. At a customer level, it can be used in collections to determine who to call first to maximize contact rates and in some cases to determine the offers as well as the sequence of offers that should be made to a customer to obtain the best results. If a financial services institution has outsourced collections to outside agencies, it can be used to determine which segments of the portfolio should be allocated to which segment for best results.

At the portfolio level, analytics can be used for stress testing and to do better loss forecasting. Many banks use customer-level data only to perform loss forecasting. However, our own experience indicates that a holistic approach using a combination of internal customer data, external data (e.g. consumer and commercial credit bureaus) as well as macro-economic variables can improve results significantly. In addition, given that some of the current macro-economic conditions are new for which reliable historical data does not exist it is important to augment models based on historical data with models that are based on simulation.

From the perspective of loan origination, many banks have reacted to the current crisis by significantly tightening underwriting standards. Our view is that the current conservative policies may have swung too far in protecting against losses. We believe that an analytical approach can yield significantly additional profits without significantly impacting portfolio risk.

3) How can analytics help banks in the long term?

We believe banks and other financial services companies need to get much smarter about using the mountains of data that are available internally as well as externally. All the financial services companies that we work only use a fraction of the data that they have access to or could get access to. In many cases the data exists in silos with no awareness of their value stand-alone or in an integrated manner. As shown in the book Competing with Analytics, companies that leverage analytics such as Capital One, stand to gain at the expense of companies that bury it. The biggest value of analytics is by surfacing insights that would otherwise be locked in databases. In short, analytics can help companies win big in the marketplace.