Consumer data analysis gave us small-ticket loan idea: Mahindra & Mahindra MD

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Published: February 21, 2020 1:15:50 AM

NBFCs must constantly understand the asset and liability side in depth because the crisis could come and hurt you from anywhere, he says.

Ramesh Iyer, mahindra financeRamesh Iyer

Mahindra Finance is focusing on greater cost efficiencies, data analysis and partnerships with other agencies to improve efficiencies in the post-IL&FS scenario, Ramesh Iyer, vice-chairman and managing director, Mahindra & Mahindra Financial Services, told Shritama Bose. The company has developed ‘social scores’ for borrowers on the basis of existing consumer data, he added. Edited excerpts:

In what ways have NBFCs evolved after the IL&FS crisis?

NBFCs must constantly understand the asset and liability side in depth because the crisis could come and hurt you from anywhere. For too long, everyone was talking about the asset side in terms of growth, NPAs, collections. Then it moved towards cost of operations and therefore, use of technology. Then suddenly came a liquidity-side crisis. So, it’s important for NBFCs to ensure a good asset-liability match and ensure they don’t borrow short to lend long. One should not always look at the cost of funds as the criteria for driving growth. In this round, this was one of the biggest realisations NBFCs would have re-adjusted themselves to.

Clearly, margins will remain under pressure, with the cost of funds and consumers’ ability to pay high rates being factors here. This will require greater efficiency from NBFCs. They will have to keep costs under control. They will have to look at consumers holistically and see whether you can help arrange loans for their other requirements. You could offer them an investment product to take care of their savings. These are various approaches to increase their ability to repay. So the things to do are to keep costs under control and to focus on consumers rather than products.

Have you changed the way you do things?

One of the things we have done is to restructure the organisation to have a product-based structure. This means someone who handles the tractor business will handle the entire tractor business from the head office up to the branch level so that their skill sets, their understanding of the needs of the customer and their cash flows are in place. They can then create branch networks which are efficient enough to deliver the product. This has been done for each product line. The other thing we have done is to use data efficiently, bringing down our overall costs as also improved the asset quality.

We’ve also invested adequately in the digital space. We are not necessarily looking at lending via the digital route, but we will enable the consumer to repay instalments digitally. While we all believe that rural ecosystems are very cash-intensive, some percentage of rural consumers are capable of repaying by digital means. That facility has been provided and that has brought down the cost of collection.
With all this experience and with the product structure that we have created, we’ve been able to build skill sets in people very differently. Therefore, the ability to improve productivity on the people side has substantially changed, which again brings down costs. Lastly, we have different tie-ups with partners and we don’t do everything by ourselves.

In which functions do you have tie-ups?

These would be in the repossession of stocks. Earlier, we used to sell them off ourselves. Now, we are partnering with other companies who have auction platforms. Similarly, we have partners for post-disbursement document collection. We have tied up with certain agencies that have deeper reach. We have opened collection centres in some places instead of setting up our own branches.

What is your AI initiative all about?

We have built a separate team which looks at all our data. Over a period of time, we have collected very rich consumer data. This pertains not just to the customer, but extends to other aspects, such as their family, its cash flows, immediate needs for expenditure and their relationships with people at the local level. We have developed a social score, apart from the CIBIL score, which accounts for elements like what the person’s relationships are, who is willing to guarantee them, etc. More importantly, in our 25-year history, we would have seen all kinds of consumers and all scenarios. So a new customer that we acquire today has to fit into the map of existing customers. They will be like one of the existing customers who have been classified into different buckets. All the behavioural features of consumers in that bucket are expected to play out for this new consumer. So we can train our local teams to react to them accordingly.

Given the needs of the consumers we have mapped, we have been able to arrive at the idea of the small-ticket loan product. The data mapping has told us at what point a consumer needs credit interventions and we have launched products to support that requirement.

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