Posts By :

Ravishankar

How Data is Transforming Corporate Relationships for Banks and NBFCs

Data has changed the way retail lending is done

Data on retail clients has made a significant impact on the way banks lend to retail customers. Various information sources such as PAN, Aadhar, and Credit Bureau scores have significantly transformed the way banks lend to individuals. Cycle times to identify, qualify, verify, and do credit checks on individuals have significantly come down too. As a result of the lower cycle times and related costs, there has been a rapid proliferation of retail loans. The current boom that we see in retail lending has its origins in ‘good data’, the story of which goes back well over a decade.

A similar transformation has started to happen in corporate lending

Very similar to the way retail lending has been transformed in India, we are beginning to see a rapid transformation in the way lending is happening to businesses, especially to the mid-sized and smaller companies. Note that currently there are over 10 lakh active companies, of which nearly 3 lakh companies have existing borrowing relationships with banks and other institutions. These figures will double over the next 5-6 years, as the economy continues to expand. The challenges to tap into this opportunity are massive for the banking industry as the stakes are quite enormous. However, the winners have much to gain. Firms that leverage data, and do it well, will have a significant head start on this journey.

How can Banks and NBFCs benefit through use of data?

Early adopters are showing the way

Sales Identification and Qualification

Today, there are over 10 lakh active companies and nearly 3 lakh existing companies with borrowing relationships in India. As this number continues to grow, it is essential for banks to use data to identify and qualify potential customers very early in the sales cycle. In today’s information-rich, digital world, unlike say five years back, it is possible to qualify prospects very early in the sales cycle. The cost of converting potential customers has dropped by nearly 90% over the past five years. Pre-qualifying the sales funnel will increase the productivity of the sales teams nearly twofold.

Also, by using data to identify the right customers, the sales teams can significantly expand their qualified target markets, at pace.

That said, transforming the sales team’s existing processes, which in many cases are still based on Rolodex-based approaches, to a more data-oriented approach is a key challenge and opportunity.

Faster Credit Analysis and Disbursement

Gone are the days where your ability to analyze a case better gave you a competitive edge. With information on prospects now widely available, speed is the only factor that will provide you with a competitive advantage. (We remember a personal loan used to take 15 days in the mid-90s, whereas we take about 30 seconds now.)

The availability of high quality, reliable information can significantly enhance the speed of decision making, thereby giving the lender a competitive edge in the marketplace. More than 50% of the information that is being collected from the borrower today is already available in the public domain.

Use of information also significantly reduces cycle time, therefore improving employee productivity. We have already seen several hours being cut out of the business credit analysis process thanks to the introduction of data.

Effective Monitoring

Scale and growth are often accompanied by their own challenges. The number of companies that have relationships with banks is rapidly increasing. Many of these companies leave a long trail of digital footprints. Regulatory requirements are also becoming stringent, making more reliable data available in the public domain, hence dependence on data is increasingly important. Active monitoring of these companies on an ongoing basis needs to be supported by data and analysis.

Amongst the significant challenges bankers face is the multiplicity of data sources, lack of standard identifiers across sources, and credibility of data sources. Given the above, it is not an easy position to be a banker in today’s world. In such a situation, technology and organized data can play a significant role in helping support the banker through the journey of monitoring.

Summary

Overall, including employee productivity, payback on the investment in data is significant. In nearly all situations, gains from using it effectively are far ahead of the cost of the data.

We see early, but strong trends, on how data availability is transforming the way business lending is done in India. Quickly and rapidly, precious minutes and hours are getting chipped away from the entire credit qualification, disbursement, and monitoring process. We are rapidly moving towards the day when a business, as long as it is good, has credit available on tap – very similar to the way retail lending operates.

Using Data for Corporate Lending

The retail lending space has changed rapidly over the past two decades, thanks to the use of data and technology. We are starting to see a similar transformation in the business lending space, through use of public data and automation. Based on the trends we have seen over the past six years, we believe that keeping the following in mind will help you get maximum mileage out of using data.

1) Measure the benefits

In most cases, there is an existing lending process. This process is done using an ‘old way’ (for want of a better term) of doing things. With the availability of data and with technology, the same process can be done more efficiently. The right way to approach the problem is to:

  • Measure the overall cost of the existing process (end-to-end cost)
  • See how the process can be changed using public data that was not available earlier
  • Gauge what the material change will be, in the overall cost of the process
  • Run a controlled prototype of the new process
  • Roll out the new process, if it makes sense

One of the challenges we see is that the approach to using ‘data’ is done tactically, without taking an integrated view of the whole process and the more significant implications.

2) SaaS model

In the new disruptive world (which, one has to admit, has made a significant change to the way we live our lives, all in just the past few years), you can use SaaS-based solutions to solve most pain points. The advantages of this model include:

  • Quicker trial and implementation: See benefits rapidly or move on if it doesn’t work out
  • Easy implementation: Does not require any significant upfront technological or financial commitments
  • Pay-as-you-go model

In a world where managers are used to conceptualizing large projects that consume a lot of time and require significant upfront investments, this approach is both critical and effective.

3) Structured vs unstructured data

There is much talk these days about AI, NLP, Machine Learning, etc. With social media and various other sources of information, the potential of unstructured data is enormous. By mining and using unstructured data mining to its potential, your process can go a long way in terms of efficiency and intuitiveness. In the process however, don’t lose sight of ‘good old structured’ information. After all, only a strong foundation of structured information can form the basis of unstructured information and analytics. Note that you can’t analyze without having good clean data in hand. Analysis without data is just an opinion.

4) Good data costs, but pays back quickly

In today’s world, led by the Internet and Google, one gets a lot of information freely. As a result, we do see a strong underlying mindset that all data is of ‘low value’. In reality, good quality data takes a lot of time to develop and money to build up. More importantly, having a foundation of useful quality data saves significant costs down the road. One would be surprised at the number of processes that exist out there, where the sole purpose is to capture and cleanse data. Instead, if good clean data had been available at the source, much of the downstream costs could have been saved. Build on a foundation of high-quality data, and the downstream benefits can be massive.

5) Keep an eye open for strategic advantages

In an industry that has been around and regulated for several decades, there are several outdated processes that have been adopted by too many people. Fear of questioning the status quo is high. Technology and data are changing things rapidly. What is even more interesting is that the regulatory system is also learning to question some of these outdated ‘requirements’, and is willing to adapt to things that benefit the end borrower (in this case, a business).

In 1995, who would have thought an individual borrower could get a loan in 30 seconds? This would not have been possible until the day someone believed ‘it can be done’.

Similarly, data and technology have started to throw up several options which question the status quo. If you understand the tools available (read data and technology), and approach it with the intent of benefiting end borrower — while bringing down overall costs, improving credit quality, and reducing cycle time – bold thinking can take you places.

The Uber, Swiggy, Paytm kind of disruptive ideas are not limited to just the B2C world. Lots can be done in the B2B world too.