How did the idea of OakNorth come to be?
In 2006, our founders, Rishi Khosla and Joel Perlman, were entrepreneurs trying to scale their rapidly growing business. Despite healthy cash flows, and strong projections for the future, they struggled to secure working capital because they didn’t have traditional assets, like property, to act as security for a loan. As Copal Amba kept growing, Rishi and Joel realised that there are millions of other entrepreneurs with growth businesses around the world facing the same struggle of securing growth capital. After scaling that business, Copal Amba, to almost 3,000 employees and selling it to Moody’s Corporation in 2014, the two took the expertise they’d developed in financial research at Copal Amba and set out develop a next-generation credit analysis and portfolio monitoring platform to redefine lending to small and medium-sized businesses globally.
Tell us more about OakNorth’s India operations, the challenges, and the opportunities you have here.
Our India team is critical to our operations and the ongoing success of the business. The team is made up of some of the best credit scientists, along with engineers who integrate into a global product team, all focussed on developing the OakNorth next-generation credit science platform. In terms of the opportunities we have in India, we’re always interested in speaking to curious, open-minded, ambitious and intellectually-motivated people who are excited about the opportunity to make a real difference to business lending globally.
What’s the scope of AL and ML in financial services?
We believe it has huge scope. However, we’re also firm believers that humans will always be a vital part of financial services and more specifically, commercial lending process. We believe that the human/computer or man and machine symbiosis will change the shape of lending and holds the key to unlocking credit issues for SMEs. While there is not enough data to produce and fit a general model that would accurately assess all corporate credit analysis cases in this class, to perform this task in a fully manual fashion requires the credit analyst to perform a very large number of tasks – some of which can be automated given machine learning techniques applied to the data we do have.
This hybrid approach is a pragmatic compromise where computers perform various tasks to allow the analyst to be more efficient, but the analyst remains in the driving seat and is able to train the models and direct and shape the final outputs to ensure they are coherent and understandable. This also means that it is not necessary to solve the entire suite of problems before automation is of some help to overall efficiency, with analysts plugging gaps in the process that are not yet automated. In fact, due to the complexity of the space, we don’t believe full automation of the entire process is a desirable end goal, and aim instead to achieve 80 per cent automation, with a human analyst always involved in the process. This allows human judgement to always have an influence on the outcome and helps ensure understandability of outputs.
How do you deploy new technologies like AI, ML, data science into your operations?
Via OakNorth Bank, we lend to profitable UK businesses and established property developers. The process is much faster than traditional high-street banks and tends to be more transparent as borrowers are invited in to Credit Committee. Our team in India, made up of credit analysts, data scientists and software engineers, use the OakNorth credit science platform to enable credit papers (the 30-40-page documents that banks’ credit committees use to make informed lending decisions), to be pulled together in days rather than the weeks or months it would normally take. The Credit Committee then review the paper and speak to the prospective borrower directly at the Credit Committee meeting. If approved, the finance will typically be in the customer’s account within days, so the entire process takes weeks rather than months. The OakNorth platform then proactively monitors the financial and operational data of the borrower, flagging up any potential issues to assist in reducing the likelihood of a late payment or default in the future.
Covid-19 has posed great trouble for the economy, how do you think technology can help commercial lenders emerge from this?
I think what the pandemic has accelerated to the forefront of the commercial lending sector is the willingness to accept and trial digital transformation. For example, the barriers historically for digital transformation has primarily been a cultural one – i.e. people don’t want to change existing processes / the way things have always been done, so blame technical barriers such as legacy IT or regulatory barriers. However, with this crisis, they’ve been forced to change and as a result, digital transformation is moving up the agenda and creating a unique opportunity for FinTechs, such as OakNorth. We’ve seen banks accelerating decisions and partnering with us at a speed that we never saw previously.
How can AI or ML, according to you, unlock the potential of customised lending to MSMEs/Large businesses?
Our next-generation credit science platform is redefining commercial lending to the “Missing Middle” – growth businesses who are the backbone of economies and communities globally, but who have been in banking’s blind spot for decades. It allows traditional financial institutions to significantly improve and accelerate their credit decisioning and monitoring capabilities in the small and medium sized business segment ($0.5m-$40m loan size) by using data science and machine learning.
Our Platform leverages machine learning, decades of credit expertise and massive data sets (including unconventional and previously unavailable data) to enhance the human. The platform enables our bank partners around the world to do the in-depth credit analysis that enables them to provide businesses with the type of bespoke deal structuring that’s typically reserved for large corporations. In doing so, the platform enables our partners to have fundamentally different conversations and engage with borrowers in a dramatically different way. It brings credit insight about borrowers’ businesses back to the front line, democratising this knowledge so that the banks relationship managers have a deep understanding of the individual business, its industry and its sub-sector. As a result, they have more relevant and thoughtful conversations with the business owner and can build much more meaningful relationships with them. Instead of wasting time on the things that don’t matter, they are able to spend more time on the things that do – structuring a loan for our borrowers’ needs in the time frame they need it, as well as exploring cross-selling opportunities.
The outcomes for the banks are:
- Improved efficiency – origination team who can transact up to 8X more deals per year
- Faster growth – targeting a wider portion of the market and completing deals in less time
- Premium pricing – higher pricing and better risk-reward
- Better credit experience for the borrower – faster transaction completion (weeks vs months) and highly customised loan facilities
- Attractive economics – structural reduction in cost income ratio
And the outcomes for the businesses is getting debt finance products quickly that are structured to their individual needs and will enable them to achieve their growth ambitions. This enables them to avoid the opportunity cost of having to wait months to get an answer and to therefore get back to running their business.