Key Highlights

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Have you bought a mobile phone lately? If you did, you probably got recommendations from a few friends, compared brands and features online, and ordered a phone that was delivered to your home. You’d have a similar no- hassle experience if you were buying a car, a DSLR or any other big- ticket product.

Have you taken out a loan lately? If you have, you’ve probably had a much more painful, confusing, dispiriting and opaque experience than you had while buying almost any other big- ticket product. Prithvi Chandrasekhar, HeadRisk and Analytics, InCred says that loan applicants typically share extensive personal details with complete strangers ( which, by the way, puts applicants at risk of identity theft). They spend days, or even weeks, not knowing what is happening to their loan application. They accept highprices, or complex, opaque prices, just because they’re sick and tired of the hassle and want to just get on with their lives.

Consequently, borrowers don’t identify with lenders, despite dealing with them monthly. They don’t reward lenders with affection or brand loyalty. There are no relationships, just transactions.

The good news is that this arrangement, this industry equilibrium, is changing fast. The balance of power is shifting towards the customer, right now, right here in India. While there are many reasons why this is happening, including demographics, regulations and technology, two of the strongest and most interesting forces driving this change are the intertwined rise of lending platforms and data- analytics.

Lending platforms are the most visible element of this emerging equilibrium. Typically, these websites allow a borrower to compare loans from different lenders and choose the best offer. What is common to all of these platforms is that ( 1) they are empowering the customer, by putting more choice in her hands ( 2) they are working, by originating thousands of crores worth of loans this year. Less visible, but arguably more important, are the data- analytics powering these lending platforms.

The most critical dataanalytics tools are related to credit and identity management. The revoltion underway is that today, with data- analytics, underwriters can do their work quicker, cheaper and better. The tools and techniques used here are very diverse, and work in conjunction, as a suite.

An even more advanced technique would be to use AI to train an algorithm to mimic the underwriting decisions made by an experienced expert, without necessarily “understanding” what the expert is doing. A second area where data- analytics is having an impact is in bringing new types of customers to lending platforms. At its best, this brings in customers who don’t think of themselves as borrowers, thus growing the market as a whole. For example, it is

The most critical data- analytics tools are related to credit and identity management.

◗ An even more advanced technique would be to use AI to train an algorithm to mimic the underwriting decisions made by an experienced.

increasingly commonplace for holidays, or surgeries, to be financed by EMIs. This might involve pitching an EMIfinanced holiday in Bali to someone surfing the net for flights to Sri Lanka. Or, in enabling post- surgery nursing at home for an elective surgery patient, who would otherwise avoid surgery altogether. Data- analytics also plays the role of match maker on lending platforms. Data- analytics solves the problem neatly, by pruning this list down to 4- 6 relevant choices that reflect the borrower’s needs and attitudes, as well as her likelihood of being accepted. Like underwriting, this pruning process is not new. Sure, consumption living standards — would have risen much faster than they would otherwise have. Higher living standards in India mean tangible improvements – roofs repaired, weddings celebrated, surgeries completed, tuitions attended, holidays taken. The highest aspiration for the industry is that it plays a key role in spreading trust through India, and therefore in raising the nation’s trajectory. Lending platforms, powered by dataanalytics, clearly have the potential to provide access to credit to large swathes of Indian society. Democratising credit should result in democratising trust, and history shows that when trust is widespread, societies thrive. So, the aspiration for lending platforms, for data- analytics and for other members of our industry ecosystem is that we catalyse India itself, by creating and distributing the magic ingredient – trust. Paradoxically, we will create that trust not by being trusting, but by being ultra- paranoid about credit and identity risk.