Financial inclusion and cash

Varad Pande and Nirat Bhatnagar have an interesting Op-Ed today in Mint about financial inclusion, and about how financial institutions haven’t been innovative to make products that are suited to the poor, and how better user interface can also drive financial inclusion. I found this example they took rather interesting:

Take, for instance, a daily wager who makes Rs200 on the days she gets work. Work is unpredictable, and expenses too can be volatile, so she has to borrow money for buying vegetables, or to pay the doctor’s fees when her children fall sick. Her real need is for a flexible—small ticket, variable amount, rapid approval—loan product that she can access instantly. Unfortunately, no institutional channel—neither the public sector bank where she has a “no frills” account, nor the MFI that she has previously borrowed from—offers such a product. She ends up borrowing from neighbours, often from the local moneylender.

Now, based on my experience in FinTech, it is not hard to design a loan product for someone whose cash flows are known. The bank statement is nothing but a continuing story of the account holder’s life, and if you can understand the cash flows (both in and out) for a reasonable period of time, it is straightforward to design a loan product that fits that cash flow pattern.

The key thing, however, is that you need to have full information on transactions, in terms of when cash comes in and goes out, what the cash outflow is used for, and all that. And that is where the cash economy is a bit of a bummer.

For a banker who is trying to underwrite, and decide the kind of loan product (and interest rate) to offer to a customer, the customer’s cash transactions obscure information; information that could’ve been used by the bank to design/structure/recommend the appropriate product for the customer.

For the case that Pande and Bhatnagar take, if all inflows and outflows are in cash, there is little beyond the potential borrower’s word that can convince bankers of the borrower’s creditworthiness. And so the potential borrower is excluded from the system.

If, on the other hand, the potential borrower were to have used non-cash means for all her transactions, bankers would have had a full picture of her life, and would have been able to give her an appropriate loan!

In this sense, I think so far financial inclusion has been going on ass-backwards, with most microfinance institutions (MFIs) targeting loans rather than deposits. And with little data to base credit on, it’s resulted in wide credit spreads and interest rates that might be seen as usurious.

Instead, if banks and MFIs had gone the other way, first getting customers to deposit, and then use the bank account for as much of their transactions as possible, it would have been possible to design much better financial products, and include more customers!

The current disruption in the cash economy possibly offers banks and MFIs a good chance to rectify their errors so far!

Intermediation and the battle for data

The Financial Times reports ($) that thanks to the rise of AliPay and WeChat’s payment system, China’s banks are losing significantly in terms of access to customer data. This is on top of the $20Billion or so they’re losing directly in terms of fees because of these intermediaries.

But when a consumer uses Alipay or WeChat for payment, banks do not receive data on the merchant’s name and location. Instead, the bank record simply shows the recipient as Alipay or WeChat.

The loss of data poses a challenge to Chinese banks at a time when their traditional lending business is under pressure from interest-rate deregulation, rising defaults, and the need to curb loan growth following the credit binge. Big data are seen as vital to lenders’ ability to expand into new business lines.

I had written about this earlier on my blog about how intermediaries such as Swiggy or Grofers, by offering a layer between the restaurant/shop and consumer, now have access to the consumer’s data which earlier resided with the retailer.

What is interesting is that before businesses realised the value of customer data, they had plenty of access to such data and were doing little to leverage and capitalise on it. And now that people are realising the value of data, new intermediaries that are coming in are capturing the data instead.

From this perspective, the Universal Payment Interface (UPI) that launched last week is a key step for Indian banks to hold on to customer data which they could have otherwise lost to payment wallet companies.

Already, some online payments are listed on my credit card statement in the name of the payment gateway rather than in the name of the merchant, denying the credit card issuers data on the customer’s spending patterns. If the UPI can truly take off as a successor to credit cards (rather than wallets), banks can continue to harness customer data.

Banks starting to eat FinTech’s lunch?

I’ve long maintained that the “winner” in the “battle” for payments will be the conventional banking system, rather than one of the new “wallet” or “payment service providers”. This view is driven by the advances being made by the National Payments Corporation of India (NPCI) which is owned by a consortium of banks.

First there was the Immediate Payment System (IMPS) which allows you to make instant inter-bank transfers. While technology is great, evangelism and product management on the banks’ part has been lacking, thanks to which it has failed to take off. In the meantime NPCI has come up with an even superior protocol called Universal Payment Interface (UPI), which should launch commercially later this year.

There is hope that banks do a better job of managing this (there are positive signs of that), and if they do that, a lot of the payment systems providers might have to either partner with banks (the BookMyShow wallet is already powered by RBL (the artist formerly known as Ratnakar Bank Limited) ).

In the meantime, banks have started encroaching on FinTech territory elsewhere. One of the big promises of FinTech (and one I’ve participated in, consulting with two companies in the space) has been to ease the loans process, by cutting through the tedious procedures banks have to offer, and making it a much more hassle-free process for borrowers.

A risk in this business, of course, has been that if banks set their eye on this business, they can eat up the upstarts by doing the same thing cheaper – banks, after all, have access to far cheaper capital, and what is required is a procedural overhaul. The promise in the FinTech business is that banks are large slow-moving creatures, and it will take time for them to change their processes.

Two recent pieces of news, however, suggest that large banks may be coming at FinTech far sooner than we expected. And both these pieces of news have to do with India’s largest lender State Bank of India (SBI).

One popular method for FinTech to grow has been to finance sellers on e-commerce platforms, using non-traditional data such as rating on the platforms, sales through the platform, etc. And SBI entered this in January this year, forming a partnership with Snapdeal (one of India’s largest e-commerce stores).

Snapdeal, India’s largest online marketplace, today announced an exclusive partnership with State Bank of India to further strengthen its ecosystem for its sellers. With this association, Snapdeal sellers will be able to get approval on loans from financers solely on the basis of a unique credit scoring model. There will be no requirement of any financial statements and collaterals.

Sellers on the marketplace can apply for loans online and get immediate sanction, thereby enabling “loans at the click of a button”. This innovative product moves away from traditional lending based on financial statements like balance sheet and income tax returns. Instead, it uses proprietary platform data and surrogate information from public domain to assess the seller’s credit worthiness for sanctioning of loan.

Another popular method to expand FinTech has been to lend to customers of e-commerce stores. And in a newly announced partnership, SBI is there again, this time financing purchases on the Flipkart platform.

State Bank of India, the country’s largest bank, announced a series of digital initiatives on Friday, including a first of its kind partnership with e-commerce giant Flipkart, to offer bank customers a pre-approved EMI facility to purchase products on the retailer’s website.

The bank, which celebrates its 61st anniversary (State Bank Day) on July 1, said the objective was to provide finance to credit worthy individuals, and not just credit card holders. The EMI facility will be available in tenures of six, nine and 12 months.

Just last evening, I was telling someone that there’s no hurry to get into FinTech since it will take a decade for the industry to mature, so it’s not a problem if one enters late. However, looking at the above moves by SBI, it seems the banks are coming faster!

 

Bonuses and federalism

I spent a couple of years working for an investment bank, and the way they would distribute (the rather hefty) bonuses in the organization was rather interesting. Each manager in the firm would receive two sums – the first was his own bonus, and the second was the bonus to be distributed among all his subordinates. If any of the said subordinates were managers themselves, they would similarly receive two sums – separately for themselves and for their subordinates.

This is pertinent in relation to the devolution of power between the states and the third level of government. Even though district, taluk and city governments have been empowered by the 73rd and 74th amendments, they don’t have much real power because their finances are controlled by their respective state governments. In banking terms, this is like giving a manager one pot, and asking him to divide it between himself and his subordinates. The incentive is obviously to distribute the minimum amount possible to keep the subordinates happy. And this is exactly what is happening to federalism in India today.

What we need is a strict rule-based formula of distribution of central government revenues between the central governments, states and the next level (rule can be made based on populations, etc.). What we also need is a requirement for states to enact similar rules to divide revenue between states, districts and sub-districts in a rule-based manner. Until this happens, true federalism will remain a pipe dream.

Provisioning for Non Performing Assets at Banks

K C Chakrabarty, a Deputy Governor at the Reserve Bank of India recently made a presentation on the credit quality at Indian banks (HT: Deepak Shenoy). In this presentation Dr. Chakrabarty talks about the deteriorating quality of credit in Indian banks, especially public sector banks.

What caught my eye as I went through the presentation, however, was this graph that he presented on “Gross” and “Net” NPAs (Non-Performing Assets). Now, every bank is required to “provision” for NPAs. If I’ve lent out Rs. 100 and I estimate that I can recover Rs. 98 out of this, I need to “provision” for the other Rs. 2 which I expect to become “bad assets”. Essentially even before there is the default of Rs. 2, you account for it in your books, so that when the default does occur, it won’t be a surprise to either you or your investors.

Now, NPAs are measured in two ways – gross and net. Gross NPAs is just the total assets that you’ve lent out that you cannot recover. Net NPAs are gross NPAs less provisioning – for example, if you expected that this year Rs. 2 out of Rs. 100 will not come back, and indeed you manage to collect Rs. 98, then your Net NPA is zero, since you’ve “provisioned” for the Rs. 2 of assets that went bad. If on the other hand, you’ve expected and provisioned for Rs. 2 out of Rs. 100 to be “bad”, and you manage to collect only Rs. 97, your “Net NPA” is Re. 1, since you now have Gross NPA of Rs. 3 of which only Rs. 2 had been provisioned for.

This graph is from Dr. Chakarabarty’s presentation, indicating the movement of total NPAs (across banks, gross and net) over the years:

Source: Presentation by K C Chakrabarty, RBI Dy. Gov. , via Capital Mind

What should strike you is that the net NPA number has always been strictly positive. What this means is that our banks, collectively, have never provisioned enough to offset the total quantity of loans that went bad. I’m not saying that they are not forecasting accurately enough – loan defaults are mighty hard to forecast and it is hard for the banks to get it right down to the last rupee. What I’m saying is that there seems to be a consistent bias in the forecast – banks are consistently under-forecasting the proportion of their assets that go bad, and are not provisioning enough for it. This has been a consistent trend over the years.

This fundamentally indicates a failure of regulation, on the part of both the bank regulator (RBI) and the stock market regulator (SEBI). That the banks are not provisioning enough means that they are misleading their investors by telling them that they are going to have lesser bad assets than actually are there (SEBI). That the banks are not provisioning enough also means that they are exposing themselves to a higher chance (small, but positive) of defaulting on their deposit holders (RBI).

How would this graph look like if the banks were provisioning properly?

The Gross NPA line would have remained where it is, for it doesn’t depend on provisioning. However, if the banks were provisioning adequately, the Net NPA line should have been hovering around zero, going both positive and negative, but mean-reverting to zero! This is because banks would periodically over and under-forecast their bad assets and provision accordingly, and then dynamically change the model. And so forth..

Read the full post by Deepak to understand more about our bank assets.

Correlations: In Traffic, Mortgages and Everything Else

Getting caught in rather heavy early morning traffic while on my way to a meeting today made me think of the concept of correlation. This was driven by the fact that I noticed a higher proportion of cars than usual this morning. It had rained early this morning, and more people were taking out their cars as a precautionary measure, I reasoned.

Assume you are the facilities manager at a company which is going to move to a new campus. You need to decide how many parking slots to purchase at the new location. You know that all your employees possess both a two wheeler and a car, and use either to travel to work. Car parking space is much more expensive than two wheeler parking space, so you want to optimize on costs. How will you decide how many parking spaces to purchase?

You will correctly reason that not everyone brings their car every day. For a variety of reasons, people might choose to travel to work by scooter. You decide to use data to make your decision on parking space. For three months, you go down to the basement (of the old campus) and count the number of cars, and you diligently tabulate them. At the end of the three months, you calculate that on an average (median), thirty people bring their cars to work every day. You calculate that on ninety five percent of the days there were forty or fewer cars in the basement, and on no occasion did the total number of cars in the basement cross forty five.

So you decide to purchase forty car parking spaces in the new facility. It is not the same set of people who bring their cars to work every day. In fact, each employee has brought his/her car to the workplace at least once in the last three months. What you are betting on here, however, is correlation, You assume that the reason Alice brings her car to office is not related to the reason Bob brings his car to office. To put it statistically, you assume that Alice bringing her car and Bob bringing his car are independent events. Whether Alice brings her car or not has no bearing on Bob’s decision to bring his car, and vice versa. And you know that even on the odd day when more than forty people bring their cars, there are not more than forty five cars, and you can somehow “adjust” with your neighbours to borrow the additional slots for that day. You get a certificate from the CEO for optimizing on the cost of parking space.

And then one rainy morning things go horribly wrong. Your phone doesn’t stop ringing. Angry staffers are calling you complaining that they have no place to park. Given the heavy rains that morning, none of the staffers have wanted to risk getting wet in the rain, and have all decided to bring their cars. Never before have they faced a problem parking so they are all confident that there will be no problem parking once they get to work, only to realize there is not enough parking space. Over a hundred employees have driven to work, and there are only forty slots to park.

The problem here, as you might discover, is that of correlation. You had assumed that Alice’s reason to get her car was uncorrelated to Bob’s decision. What you had not accounted for was the possibility that there could be an exogenous event that could suddenly drive the correlation from zero to one, thus upsetting all your calculations!

This is analogous to what happened during the Financial Crisis of 2008. Normally, Alice defaulting on her home loan is not correlated with Bob defaulting on his. So you take a thousand such loans, all seemingly uncorrelated with each other and put them in a bundle, assuming that 99% of the time not more than five loans will default. You then slice this bundle into tranches, get some of them rated AAA, and sell them on to investors (and keep some for yourself). All this while, you have assumed that the loans are uncorrelated. In fact, the independence was a key assumption in your expectation of the number of loans that will default and in your highest tranche getting a AAA rating.

Now, for reasons beyond your control and understanding, house prices drop. Soon it becomes possible for home owners to willfully default on their loans – the value of the debt now exceeds the value of their home. With one such exogenous event, correlations suddenly rise. Fifty loans in your pool of thousand default (a 1 in gazillion event according to your calculations that assumed zero correlation). Your AAA tranche is forced to pay out less than full value. The lower tranches get wiped out. This and a thousand similar bundles of loans set off what ultimately became the Financial Crisis of 2008.

The point of this post is that you need to be careful about assuming correlations. It is to illustrate that sometimes an exogenous event can upset your calculations of correlations. And when you go wrong with your correlations – especially those among a large number of variables, you can get hurt real bad.

I’ll leave you with a thought: assuming you live in a primarily two wheeler city (like Bangalore, where I live), what will happen to the traffic on a day when 10% more people than usual get out their cars?

Exponential increase in uptake of IMPS

We had dealt with exponential increases on this blog once before. We revisit the topic, and this time this is in the context of the inter bank mobile payment system that came into place sometime last year. I’ve never used it so I’m not sure how it works, but going by the data put out by the National Payments Corporation of India, the volume of transactions is increasing at an exponential rate.

How do we determine this is an exponential rate? First, let us look at the time series of total volumes of transactions:

Source: http://www.npci.org.in/impsVolumes.aspx
Source: http://www.npci.org.in/impsVolumes.aspx

Notice that after remaining flat for a couple of months (maybe even decreasing) the number of transactions has really taken off (March is probably an aberration – but given that it’s the month of financial closure the higher volumes can be expected). Increased exponentially, you say? How can we test that?

We can test that by using a logarithmic scale for the y-axis. Here is the same plot again, except that this time the Y-axis is logarithmic.

Source: http://www.npci.org.in/impsVolumes.aspx
Source: http://www.npci.org.in/impsVolumes.aspx

Notice that apart from the part with the aberration and the initial two months, the graph is now linear. In other words, we can describe this graph by a line of the form

log y = a + b x

or y = exp (a + bx)

Thus, exponential!

Coming back from the geekery, it is really good to note that IMPS has taken off. However, this should not be taken as proof of the fact that mobile payments are easy, for IMPS is anything but easy. New RBI Governor Raghuram Rajan has said in his inaugural speech that he hopes to make it simpler to make payments via mobile. Hopefully this will take off soon. Till then all we can do is to contribute to the exponential growth in the update of the IMPS!