Shweta Gupta, VP – Technology at Digital Vidya: FinTech is revolutionary, to say the least. Technology has entirely changed the Banking and Finance industry. The use of technology in the BFSI industry is not limited to online transactions, stock trading and mobile banking. It goes far beyond to Customer Segmentation, Fraud Detection, Risk Management, Personalised Services etc. Generally, due to the nature of the industry, we don’t consider the great work startups are doing, because our eyes are mostly fixed with the giants in the financial sector. I caught up with Mohit Bansal, Principal Data Scientist of CreditVidya and spoke at length about his work in the company. It was a refreshing conversation that extended my horizons of knowledge about Data Science and the BFSI Industry.
Without further adieu, I invite you to read his Job Function Email List wonderful revelations. mohit bansal Mohit Bansal graduated from IIT Dhanbad and immediately joined Mu Sigma as a Trainee Decision Scientist. Currently, he works at CreditVidya (a FinTech Company) in the capacity of a Principal Data Scientist. Interestingly, Mohit was the first employee in CreditVidya’s Data Science department and they are a bunch of 20+ in the same department, today. How did you get into Data Analytics? What interested you in learning Data Analytics? Mohit Bansal: I was running my startup, Techaloo.com during my college days where we were covering various startups in Asia.
After interacting with various founders and hearing about the power of Big data from them got me interested in the field of Data Analytics. Then I started applying simple Data Science techniques in lab experiments, for example- understanding the impact of temperature, humidity and other factors on variation in speed of autonomous robot controlled by microcontrollers. What was the first dataset you remember working with? What did you do with it? Mohit Bansal: I started working for a US based client who was a major Yellow-pages Directory Company as my first project. We were trying to understand the drivers of NPS (Net Promoter Score) at various sales stages across the customer lifecycle. We used text mining, topic modelling, clustering and classification techniques to understand the driving factors for NPS ratings. Was there a specific “aha” moment when you realized the power of data.