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Varun Ravichandran

The Information Imperative – Part II

To appreciate why efficiencies driven by information are crucial to the banking sector, let us do some number crunching. Note: All data below are results of Probe research.

Broadly speaking, banks’ income can be categorized into interest income (interest on loans to borrowers) and non-interest income (fees and commissions, profits from trading, etc.). On the expense side, we have interest expense (interest paid on deposits), and operating expenses (sales and marketing, legal fees, credit and risk analysis, employee compensation and benefits, etc.).

So to get a sense of a bank’s margin spread, we can look at:

Spread = ( InterestIncome/TotalFunds + (Non-InterestIncome)/TotalFunds ) – (InterestExpense/TotalFunds + OperatingExpense/TotalFunds)
Let’s see what the data is telling us. We will use consolidated data on all major Indian banks over the last 25 years, starting at 1992, right after liberalization.

Here is how interest income is trending over this quarter-century:

The Information Imperative

Over the course of the ‘90s, interest income began trending upwards, reaching a high-point of 11.22 in 1997. Over the ‘00s and ’10s, this metric stabilized, with a gradual trend downward. In 2016, interest income to total funds stood at 8.44.

Over the next few decades, interest income will continue to trend marginally downward; loans will get repaid or renegotiated at lower rates as the economy matures. While the Indian market still has sufficient room to grow until it reaches maturity, the end state is inevitable — interest income growth will not keep rocketing upward.

Non-interest income (primarily fees) has meanwhile remained steady at around the 1 mark over the last 10 years. In fact, it has declined from its high point of 2+ in the mid-00s. Indian banks still are quite heavily dependent on interest income, and have not diversified their income streams to the extent that banks in advanced economies have.
Now let us look at the expenses:

Interest expenses, which fluctuate depending on rate cuts and hikes by the RBI, have centered around the 6-mark.
Over the 90s’ and 00’s, operating expenses were brought down from around 3.5 to about 2. This was due to efficiency-improving factors such as information technology. Over the last decade, though, operating expenses have remained flat.
Let us now merge the 2 pictures to look at the margins over these 27 years:

As you can see, incomes and expenses have been moving up and down more or less in tandem over the last two and a half decades. But with interest incomes trending gradually downward, with all else being the same, margins begin to get squeezed in the coming years.

In such a scenario, how will banks ensure they maintain (and improve) their spreads? How do they stay above water?
They have two levers at their disposal — increasing non-interest incomes, and decreasing operating expenses.
The first of the two — increasing fee and other non-interest incomes — is quite limited in the amount of heft it can provide. The increased competitiveness of the financial services marketplace — and hence the market’s price sensitivity — places hard limits on this lever. Which leaves us with the second option — reducing operating expenses.

To remain competitive and profitable, banks need to launch a new wave of efficiency enhancements. While the earlier wave (in the ‘90s and ‘00s) was driven by systems integration and the Internet, the next round will be driven by data and analytics.

With easy access to relevant and rich data, banks can drastically improve targeting and decrease cycle times across the board, whether in sales or credit disbursement. Data can drive accurate credit and risk analysis, bringing down the proportion of NPAs. High-quality on-demand information can drive down customer acquisition costs. Portals and APIs can load data automatically into credit models to display rating scores in seconds. In the long run, data will enable sharper targeting and predictive analytics, which will not just bring down costs but also propel growth.
In summary, to stay competitive, banks need to reduce OpEx; and to reduce OpEx, banks need data.

Closing the Information Gap

The need of the hour is for on-demand information on Indian companies — information that is comprehensive, validated, and clean. Sales, credit, and risk teams need to base their decisions on hard data; they need to expand their models to incorporate information on companies’ legal history, defaulting history, directors’ track records, etc. They need to understand the connections between companies — via common directors or owners, for instance — to estimate risk and foresee ripple effects.

The path to a more profitable and sustainable future for banking in India is paved with data and analytics. Ultimately, closing the information gap will result in a fairer and more competitive marketplace, and will power us toward a better India. And that is exactly the vision that Probe Information Services is working toward.

The Information Imperative – Part I

The digital revolution is reshaping industries across the globe, with data analytics being a key driver of this transformation. Insights gained from data are integral to the strategic decision-making apparatus of companies, enabling cost reductions as well as revenue growth.

Among the industries with the potential to benefit most from this data-powered digital transformation, Financial Services is consistently ranked at — or close to — the top. Whether in determining future product offerings, getting a better handle on risk and credit worthiness, or in cutting costs in the customer acquisition process, the use of data is beginning to redefine banking across the globe.

Data is Enabling Consumer-centricity

According to EY’s Global Consumer Banking Survey, 70% of consumers from around the world are willing to provide more data on themselves, if it results in more relevant products and services being offered to them. Consumer banking is taking the lead in leveraging data, in large part due to the proliferation of relevant data sources. For instance, banks are utilizing customers’ social media feed to determine what products and services to offer. Diverse data points such as social media friend-lists, online shopping behavior, and telecom bill-payment history are used to rank customers by credit-worthiness.

Indian banks are leading by example in this regard; from large players like HDFC and ICICI to smaller regional players, the systematic use of consumer data is resulting in vastly reduced credit disbursement cycles, lower risk, and superior customer relationships. In addition, government initiatives such as JAM (Jan Dham Aadhaar Mobile) are decreasing the information gap between banks and their customers, further enabling the move toward a data-driven future.

On the commercial banking side, though, the picture is not so rosy.

Commercial Banking – The Data Deficit

India Inc. is massive, fast growing, and very opaque. And largely unlisted. While the importance of incorporating data into commercial sales and lending decisions is well appreciated, the sheer lack of access to real-time, high-quality information has been a hurdle for banks. Information on companies — particularly unlisted companies, that make up 99% of the economy — has traditionally been:

Unreliable — Given the unavailability of third-party validated data, banks rely on documents provided by the very companies they are evaluating, exposing themselves to potential fraud. In addition, the information gathered is often out-dated.

Incomplete — To gain a solid handle on credit risk, a variety of information is required — from financial statements and ownership structures to legal and compliance documents and more. Banks find it hard to pull together all of these data-points, resulting in an incomplete picture.

Fragmented — The information that banks are looking for is spread across various disconnected sources. Credit teams spend long cycles pulling together notes from MCA, credit bureaus, ratings agencies, etc.

This information gap explains why the cost of credit to business borrowers in India is significantly more than that to individual borrowers. It also partly explains the headlines in our business papers, which talk about the large NPA (non-performing assets) problem facing the country.

Why is information-driven efficiency important for banks?

In Part II, we will pick apart the numbers that make up banks’ bottom-lines, to understand why information-driven efficiency is not just a luxury but, in fact, a necessity.

View Part II