Praveen Agrawal, Co-Head of India, OakNorth, has 18 years of experience in extending financial services to large investment banks and firms. As a seasoned professional, Praveen is known for his exceptional analytical skills and attention to detail. In his current position at OakNorth, he co-manages their operations in India, comprising of a team 500 plus employees with skills ranging across multiple domains namely credit analytics, technology, data-science, consulting and presales. Praveen oversees the strategic direction of the team for enhancing their overall performance to achieve the company’s mission to transform the SME lending scenario in India.
Ensuring that scale-ups across the globe are given the financial support they need to continue their growth journeys is vitally important. These businesses, known at OakNorth as the ‘Missing Middle’, are the most significant contributors of economic and employment growth, yet still struggle to access fast, flexible debt finance.
Our co-founders, Rishi Khosla and Joel Perlman, found this out first-hand when in 2005, they were looking for a working capital facility to support their growing business, Copal Partners, a financial research firm they’d founded three years previously. They approached numerous high-street banks and kept getting variations of the same response – “the computer says ‘No’”. Despite being a profitable business with strong cash flow and retained clients, none of the commercial banks were willing to lend to them. It was too small a ticket to offset the costs the bank would incur in doing a fundamental assessment of their business and structuring a finance facility for their needs. A few months later through one of their institutional client’s special situations desks, they managed to secure 100x the amount of debt for a dividend recap. So, an institutional division of a bank was able to support them, but the commercial lending part of the bank was not.
Scale up businesses all over the world have had similar experiences. Finding the optimal balance between offering a great customer experience and managing the cost to serve has proven difficult. Loans are either too big for automated decision models or too small for the unit economics of banks’ manual approach to make commercial sense. Commercial lending has consequently been characterised by slow, commoditised, computer-says-no lending, rather than fast, bespoke, customer-centric lending. Commercial lending has become more and more commoditized, leaving commercial banks feeling like pricing is the only way to differentiate from other lenders. This race to the bottom has become an expensive proposition for banks looking to grow their commercial books.
In order for banks to lend faster, smarter and more to scale-up businesses, there are a number of areas they can look at:
Take a forward look view
A typical high-growth business grows at 20% year on year, so the difference between its last 12 months and its next 12 months, is c.40%. So, if a lender is only lending to this business based on its historic data / past performance, they’re not lending to it based on what’s needed to support its current or future growth trajectory. This is akin to driving by only relying on what you can see in the rear-view mirror as opposed to driving looking ahead through the windshield.
It’s so important for lenders to use data and insights to build a clear picture of a business’ future growth potential and where it’s going, rather than simply relying on where it’s been. Unfortunately, so many lenders still see high-growth as high-risk, and because they’re unable to develop a reliable forward-look view of a business, they’re unable to get comfortable lending to them.
Build a granular, loan-level understanding of the business
Many traditional lenders tend to lump all businesses into one of a dozen or so categories – for example, all restaurants, hotels, bars, leisure facilities, etc. fall under “hospitality and leisure”. There are a couple of issues with this approach – firstly, it ignores the unique differences between businesses within the same sector, and secondly, human bias can mean that certain relationship managers or teams are reluctant to lend to a specific business because it falls in a sector they have had a negative credit experience with in the past. This issue was thrown into the spotlight over the last two years with COVID as businesses within similar sectors or sub-sectors had vastly different experiences at different stages of the pandemic.
Take an airport hotel and a countryside hotel catering to hiker and cyclist staycationers for example – both would be classified as hotels, but their experiences over the last two years will have varied significantly. The airport hotel would have experienced a dramatical fall in sales due to heavy restrictions on international travel, however the countryside hotel catering to staycation holidays would have seen a large uptake in reservations, again as a result of the restrictions to international travel.
Developing a granular, loan-level understanding of a business will therefore enable them to structure a facility that’s bespoke for that business’ needs.
Use a range of data to build a fuller picture
When it comes to commercial lending, traditional lenders need to assess the ability of a business to sustain a certain level of debt and repay loans. This is where data science comes in.
The only way they can effectively assess commercial credit risk is by using multiple data sources – including what may be unconventional or previously unavailable data – rather than just relying on what they’ve used in the past. An example within the hotel industry is using reservation data from websites such as Booking.com or Trivago and for the restaurant sector, analyzing reviews from the likes of TripAdvisor and Yelp.At OakNorth, we have built one of the largest US commercial lending data sets globally, and pipe in over 400m external third party and alternative data sources which help provide an overall perspective of what the “now” looks like.
Use early warning indicators to identify potential headwinds early on
As demonstrated by the COVID-19 pandemic, when it comes to adverse events, the traditional approach to commercial lending – using historical data, financial modelling of a base case, worst case and best-case scenario – is an approach that is not fit for purpose. In uneventful times, these models are fine. However, for unprecedented events such as the pandemic, the traditional models proved useless as historical correlations were broken; employing the traditional look-back approach was meaningless.
By utilising early warning indicators that are able to run a “bottoms-up” analysis of their loan books, as well as a forward-looking credit scenario that takes into account liquidity, debt capacity and profitability, banks will be able to proactively identify and take action on borrower level risks before it’s too late, minimizing their losses.
Looking ahead, the world unfortunately has numerous macro headwinds to contend with – the Russia/Ukraine war, climate change, rising inflation and interest rates, supply chain issues to name a few – making the need for scale-up businesses to be supported by trusted lending partners more important than ever. Commercial lenders will therefore play a vital role moving forward and they’ll be able to lend faster, smarter and more by following the approaches detailed above.