Value lending for MFIs to reduce risk

Tara Thiagarajan, chairman & MD of non-banking finance company-micro finance institution (NBFC-MFI) Madura Microfinance, is trying out a unique experiment.

Tara Thiagarajan, chairman & MD of non-banking finance company-micro finance institution (NBFC-MFI) Madura Microfinance, is trying out a unique experiment. A PhD in neuroscience from Stanford University, she has been pioneering the use of analytics in the MFI sector, which, she says, has helped Madura to be among the most cost-efficient MFIs worldwide. In an interview with Arun S, she explains why her concept of ?value lending? is crucial to the sector.

How have you been using your neuroscience background and analytics to make Madura very cost-efficient?

We have been using science and analytics, not primarily on cost, but to understand the rural ecosystems in which we work, so that we can design new products that can be more effective and better manage the risk. Lending today is very commoditised and, sometimes, can be detrimental to the larger system, creating an unproductive debt burden rather than economic growth.

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Many MFIs have suffered losses as a result of insufficient client data. Can you explain the concept of ?value lending? and why it is important for the sector?

About 90% of India?s labour force operates in an informal economy that is ?data dark? ? where there is no documented record of economic activity and very little is known about the individuals involved. There is a wide variation in the economic outcomes of individuals and enterprises. However, in the absence of data, we cannot identify those who have the talent and capability to scale up and drive economic growth. Therefore, loans are currently structured in way so as to mitigate risk.

As opposed to this, ?value lending? is about allocating capital more efficiently to those with potential to drive economic growth. We are using mechanisms we have developed for ?dark data? acquisition and analytics to enable a steady shift in this direction. Our research has yielded several ?proxy variables? that are highly predictive of entrepreneurial behaviour and enterprise outcome.

The more intelligent the MFIs can become in allocating capital efficiently to productive enterprise, the more positive impact it can have on the society and reduce risks of lending.

Bandhan Financial Services has recently got banking licence, the first NBFC-MFI to enter the sector. With RBI keen to give licences ?on tap; basis, will more NBFC-MFIs apply for bank licence?

There are a few NBFC-MFIs that might seek banking licenses. Being a bank enables them to take deposits and reduce the cost of capital. However, there are considerable challenges for an MFI in this regard as the MFI customer base is from low-income segments where deposits cannot be expected to be a significant percentage of loans extended. Therefore, to be successful in building deposits and compete with other banks, MFIs must begin to tap the urban corporate and HNI segments.

Bandhan, set up in 2001, has become the country’s largest MFI with 2016 branches across 22 states, a 55-lakh customer base and an outstanding loan book of R6,200 crore. Though Madura started operations in 2006 and despite the use of analytics, it has disbursed loans worth only R900 crore to 5 lakh customers and has just 160 branches that too only in Tamil Nadu. Why has your growth been very slow?

In the years leading up to the Andhra crisis, we were seeing the destructive effects of indiscriminate lending in the field. Rather than follow the trend of creating stress in the system to seek short-term profits, we chose to grow at a rate that could be fuelled by our internal accruals and use the time to understand the system and think through ways to approach lending that would create greater systemic value.

This is a long journey, but we are moving steadily towards creating a more intelligent system for lending that can scale rapidly and still create greater value per rupee. As we get increasingly confident of the positive impact we are having, we will scale faster.

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First published on: 11-04-2014 at 04:30 IST
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