Mumbai-based Suryoday Small Finance Bank (SSFB), morphed from a microfinance company to a small finance bank over a period of 8 years. The strong focus of the bank is to serve the unserved and the underserved through innovative banking practices.
The bank has a dedicated data analytics team. We spoke to Rahul Kotabage, who leads the Business Intelligence and Analytics division at Suryoday Small Finance Bank. Rahul has worked in assurance audit with PWC for 7 years and then 4 years with Suryoday Small Finance Bank.
Analytics India Magazine: How does Suryoday Small Finance Bank utilize analytics?
Rahul Kotabage: We are servicing the customers from the bottom of the pyramid hence first we have focused on building stronger data pipelines pertaining to these customers which can lead to better analytics.
Presently we use analytics for the core aspects of lending and deposit business, for lending business we use analytics to identify cross-sell and up-sell potential in our customer base, risk management and prioritization basis customer analysis, new market and congruent market reviews. For the deposit base, we are doing customer segmentation for better cross-sell and will be getting into transaction analytics for better insights and use.
AIM: Can you point to some specific analytics use cases that brought value to the organization?
RK: Up-sell analysis has helped us program our pre-approvals to the customer base in a more methodical manner and thus help build risk-averse loan book which has a lower probability of defaults and has also resulted in catering to the customers when there is need at their end. Further, new market analysis and geo-analysis have helped us identify locations where we have been able to deploy resourced efficiently and get better returns for the business and expand efficiently.
AIM: How is the analytics group structured, team size, under which department etc?
RK: We have always designed the team methodically and kept it nimble. Roles are mixed but to make buckets we have 3 data scientists in the team and we have 4 business analysts with capabilities of doing list cutting and base level modelling themselves. Its a young energetic team and I try and lead them to deliver what businesses want and then what we read into lots of data and want businesses to understand and use.
AIM: What is the biggest challenge you face while implementing data-driven decision making for your organization?
RK: Data is not necessarily the only dimension for decision making thus I believe in getting the practical know-how as well embedded into the same, microanalysis which comes from the field in our business. It is difficult to establish but results in inefficient decision making.
Further, as I stated in first response, the customers in the segment we cater to have very less amount of data apart from their financial footprints thus establishing other traits to make a full view decision becomes a challenge which we have taken up to resolve.
AIM: How did you start your career in analytics?
RK: I have always been a keen person to understand the why behind the numbers I see. As a CA on my audit assignments I was always keen to have that full grasp an then also understand what the same meant from estimation perspective. In some basic form, I have always been implementing in my approach the first 2 aspects if analytics – Descriptive and Diagnostics which I still believe are very crucial in analytics consumption. Currently, I have strongly taken up the predictive aspect and in the near future, I am keen to deploy efficiency prescriptive / recommendation systems.
AIM: What are some of the things that the head of analytics function should keep in mind?
RK: I think it’s very important that we keep the basics right. We sometimes tend to get ahead of ourselves on model development and use of newer methodologies which is important without a doubt, but we need to establish that we have our data right, we understand our data in detail, its components and its implication, which then helps us work in a much better manner.
AIM: What are the biggest trends that you are seeing in the analytics industry?
RK: I think some of the work which is happening in the industry is really good, especially work around NLP and conversational analytics, text analytics especially for the medical industry. Explainable AIs are going to become more and more important for wider use and acceptance and critical for regulated industries. There are many aspects which are still being developed and will make analytics more seamless and adoption would be critical when these come around for use.
AIM: Anything else that you would like to add
RK: Analytics and AI will help scale the outreach to millions of the customers who have been categorized as under-served and un-served and we note that already today beyond just the technology intervention. While there has been a lot of work done by micro-finance institutions and now SFBs alongside development funds, the technology and capabilities that can be developed by integrating data are potentially limitless. I believe the growth is exponential for us to cater to the customers in a meaningful manner, we need not rush but apply the relevant methodologies to take them up one step at a time.
