As part of the $7 billion TVS Group, TVS Credit Services empowers Indians from various socioeconomic backgrounds with financial products. Working for the cause of financial inclusion they serve over 2.5 million customers by providing loans for two-wheelers, three-wheelers and tractors. With many new initiatives such as TEDDI, a framework to implement innovative ideas, and GURU, a mentorship program to help new employees, they have also extensively invested in adopting business analytics.
Analytics India Magazine got in touch with Dhinesh Rajamanickam, who is the chief manager of the business analytics division at TVS Credit Services to understand the analytics adoption scenario in the company. Rajamanickam talked about the role of analytics in various processes like loan approval, adoption of tech in financial services, his analytics journey and more.
With 11 years of experience in the analytics industry, he takes the credit of building the analytics team at TVS Credit from the scratch. Before TVS Credit, he worked with Fidelity Investments for eight years where he handled analytics projects related to customer acquisition, retention, cross-selling and upselling, among others.
Analytics India Magazine: Please tell us about your journey in analytics. What are some of the analytics solutions that you have worked on?
Dhinesh Rajamanickam: My analytics journey has been overwhelming so far. Throughout my 12 year career, I have been working in the financial services industry. I initially worked for the US market and for the last four years I was working in the Indian NBFC industry. I also built predictive models to identify potential delinquent customers.
AIM: How is TVS Credit using analytics?
DR: We at TVSCS use analytics throughout the customer lifecycle — starting from acquisition, to retention and maturity.
AIM: How does analytics come into the picture for various steps involving loan approval or credit scoring?
DR: Credit scoring is vital since it helps to identify risky customers upfront. Now this is a tried and tested approach, so it is time to think about unconventional approaches using alternative data.
AIM: What are the other challenging areas that you use analytics for?
DR: People management is a tricky area since diverse employees come with varied aspirations, goals and capabilities. Especially when the majority of resources are field force analytics helps to identify patterns and predict likely behaviour.
AIM: How has the adoption of analytics and related tech evolved over the years in the financial services industry?
DR: Nowadays we are dealing with an abundance of data combined with ease of storage and superior computing power. This combination paves the way for a lot of real-time analytics to take instant decisions and respond faster.
AIM: How has analytics and related tech been instrumental in expanding the product portfolio over the years at TVS Credit? What are the major analytics tools that you use?
DR: When new products are launched new opportunity to cross-sell and up-sell to existing customers come along. Analytics play a major role in identifying propensity. We use open source tools such as R Studio and Python to build statistical models.
AIM: TVS Credit recently joined hands with Zone Startups India to help these startups set a foot in the tech-driven world. Is TVS Credit intending to leverage latest technologies by these startups in its various offerings? If so, how?
DR: Yes. TVSCS is open to new innovative ideas. Any solution that reduces the turn-around time and improves the customer experience will be explored.
AIM: Are technologies such as Robotic Process Automation and AI central to TVS Credit? What are the various applications that you wish to use these technologies for?
DR: Mundane repetitive activities can be automated to make people more efficient. We will be exploring bots and AI in a few areas of work.
AIM: What are the challenges you face being in the tech space?
DR: Though there is phenomenal growth in technology, providing credit to first-time borrowers is tricky as the credit assessment is time and effort consuming.
Try deep learning using MATLAB