• News
  • Columns
  • Interviews
  • BW Communities
  • Events
  • BW TV
  • Subscribe to Print
BW Businessworld

Our Target Market Is Primarily Borrowers With Informal Income: Rupa Basu, Shubham Housing Finance

In an interview with BW Businessworld, Rupa Basu, Director- Risk, Operations and Technology, Shubham Housing Finance, talks about technology, risk management and more

Photo Credit :

What’s your target market and what led to you zeroing in on that particular segment?

Our target market is primarily borrowers with informal income in the urban outskirts across Tier I, II and III cities. This market has been largely underserved by other financial institutions due to the lack of proper income documentation. While there is an intent and ability to pay regular instalments, the borrowers lacked the necessary evidence to support it.

In our years of working with Indian and foreign multinationals, we were creating products for the affluent / semi-affluent segments while there was a lot of demand for similar products and services in the less affluent sections of society. We started Shubham when we realised the tremendous market potential as well as social impact of such products.

Tell us a bit about your deployment of Machine Learning & AI for loan processing.

When we first started disbursing loans there was not much data available for this customer segment. Our process then and still does largely depend upon formal discussions that we have with our customers to understand their entire eco-system. It’s a very human interaction driven model. 

However, being one of the first entrants in the market, we have now gathered a tremendous amount of data pertaining to customers who have applied for a loan with us as well as the properties that are mortgaged or proposed to be mortgaged with us. Combined with this we also have a wealth of information available from credit bureaus. All this information put together is being used to develop application & behavioural scorecards and predictive models to improve loan processing and collection efficiency. The large quantum of property related information is being used to build a property price index for monitoring real estate trends at a micro market level.

What factors set you apart from traditional financial institutions, from a risk management standpoint?

When we started out, we did not have any reference points or guidelines on how to serve this customer segment unlike traditional financial institutions. Hence we literally built everything from scratch including our credit policy, procedures, IT platform and independent risk management framework. We consciously did not carry forward the risk filters that we applied in our previous jobs but suitably modified our approach for this segment. Over the years we have accumulated significant information, insights and field experience that has enabled us to build customised industry / occupation specific credit appraisal templates to cater effectively and efficiently to this segment.

What role does technology play in increasing your reach to the informal segment?

Technology plays a very important role. We strongly believe technology to be an enabler to cater to this segment. With the advent of low-cost data plans and increasing mobile phone penetration, we can now reach out further to customers who do not know about us. Today it is important to go to where your customers are, and we are doing just that with mobile enabled software supporting our loan fulfilment and collections processes. Creating an omnichannel experience has been our focus over the last 6-8 months. We will soon launch our app to further enhance customer experience and improve service delivery.

How has analytics and big data added value to your portfolio?

We strongly believe in fostering the culture of data driven decision making in all aspects of business. We have set up our analytics department relatively early in our journey to achieve this. We have built best in class dashboards that provide very detailed and granular insights on portfolio quality across multiple dimensions. These enable informed decisions on various policy criteria like caution profiles, geographies, employment type, debt burden etc. In addition, we also have detailed business performance metrics and balanced scorecards that enable us to evaluate performance of our distributed branch network effectively and take strategic decisions on branch expansion or consolidation. While we have yet to reach the big data stage in our evolution, we are on track to making sure that we continuously build and evolve our models to provide best in the class service to our customers

Lastly, what are your immediate plans on the tech front, for the next 12-18 months?

To cater to rapid expansion of business, we plan to enhance our technology platform by enabling an end to end digital lending process across application fulfillment, customer service and collections. We will also explore blockchain based avenues for financial inclusion. We also plan to build real time risk decision systems using analytic models. We expect these initiatives to result in enhanced customer experience while reducing cost and time of delivery for the company.

Tags assigned to this article:
personal finance