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, Head- Risk 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 high-prices, 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.
This dysfunction is not new. Lending is a very old industry. Through most of its history sellers, lenders, have held the balance of power. As a result, customers, borrowers, just learnt to accept shabby treatment. Sort of like the way we learnt to accept standing in the sun for hours to get a US visa.
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. There are hundreds of variations on this theme that are already operational: e.g. platforms that specialise in a demographic, like students, or BPO staff; platforms that advertise on national TV and offer all loans to all customers from all lenders; platforms that specialise in a product, like say used cars or education; platforms that specialise in a service, like helping borrowers to use their social or educational capital to get a loan. 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 data-analytics tools are related to credit and identity management. These are used to check if the borrower is who she says she is, and if she has the wherewithal to repay the loan. This is, of course, what underwriters have been doing since time immemorial. The revolution 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 example of an important but basic tool would be a query to the credit bureau to find out if an applicant has multiple PAN numbers, a classic sign of fraud. A more advanced technique would be to present multiple products offers to an applicant, to understand the applicant’s mentality. For example, an applicant is shown two hypothetical loan offers, one for Rs 5 lakhs at 12 per cent p.a. and another for Rs 10 lakhs at 20 per cent per annum. An applicant who prefers the second offer clearly has a very different mind-set and would need to be treated very differently, from the one who prefers the first. An even more advanced technique would be to use artificial intelligence to train an algorithm to mimic the underwriting decisions made by an experienced expert, without necessarily “understanding” what the expert is doing. This is like a chess-playing algorithm that mimics a grandmaster’s play, without directly “understanding” or explicitly formulating the grandmaster’s method.
Taken together, these credit tools have already opened up credit access to millions of Indians. Today, if a creditworthy applicant looks for a loan on a platform, she gets credible offers from multiple lenders. This means her primary bank, who always knew she was creditworthy, has to make her more competitive offers. This is a very real power shift between borrower and lender and will continue to gather momentum.
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 increasingly commonplace for holidays, or surgeries, to be financed by EMIs. How fast, and how far, this trend develops depends on how adept lending platforms get at marketing - at pitching the right loan offer to the right customer at the right time. This might involve pitching an EMI-financed 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. These applications lend themselves very well to analytics-led optimisation, typically in micro-targeting very specific messages to customer niches, conducting extensive A/B testing and optimizing with machine learning.
Data-analytics also plays the role of matchmaker on lending platforms. Very soon, the Indian borrower will be presented with a bewildering array of product choices. For instance, Bank Bazaar has 58 distinct credit cards listed today. That list will grow. Moneysupermarket, a successful UK marketplace, has over 400 credit cards listed on any day, way too many for anyone to comprehend. This can flip the customer from one sort of powerlessness (no choice) into another (overwhelmed by choice).
Data-analytics solves this 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. Financial advisors have long played a similar role for HNI customers, helping them navigate a maze of choices. Data-analytics brings that expertise to the mass-market.
In summary, lending platforms powered by data-analytics are here to stay. Competition is intense, and inevitably many platforms will fail. But some will emerge as winners, and collectively they would have changed the way the industry operates forever.
And when this cycle of creation and destruction has played out, what good would have emerged?
Sure, consumption - living standards — would have risen much faster than they would otherwise have. That is great. Higher living standards in India mean tangible improvements – roofs repaired, weddings celebrated, surgeries completed, tuitions attended, holidays taken. This is welcome and natural in a young, fast-growing country.
But my view is that something much more valuable can also be created. Harvard sociologist Robert Putnam has long championed the theory that the crucial difference between successful societies and failed societies is one ingredient – trust. His point, well supported by research, is that enhancing the level of trust in a society dramatically raises the trajectory of that entire society.
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 data-analytics, clearly have the potential to provide access to credit to large swathes of Indian society. In doing so, they should also spread trust, as every loan given out is ultimately an act of trust, and every loan repaid is an honouring of that trust.
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.
