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Interview with OakNorth India’s Praveen Agarwal

November 20, 2019
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OakNorth, one of Europe’s fastest-growing fintech companies, is leveraging digital technologies to help traditional financial institutions improve credit decision-making. The company gathers a wide range of relevant internal and third-party data sets that help in credit analysis, which is majorly done by the company’s India team. In an interview with tele.net, Praveen Agarwal, Managing Director, OakNorth India, talks about the company’s key focus areas and how technology tools like AI can be used to make more informed credit decisions....

Tell us more about OakNorth and its business.

OakNorth is one of Europe’s fastest-growing fintechs by assets, valuation, profits and revenue. The business’ mission is to provide small and medium-sized growth companies with the debt finance they need to compete against large corporates.

Our big data and machine learning platform enables us to do this. Within the UK, we do this via balance sheet lending (OakNorth Bank Plc) where we lend between £0.5m-£40m. Throughout the rest of the world, we do it with by licensing our next-generation credit platform to other banks and lenders so that they can replicate our success with small and medium enterprise (SME) lending in the UK, in their own markets. We now have 12 banks/financial institutions using the platform across Asia, North America, Australia and Europe, with total assets equalling $12 billion.

Since its inception, OakNorth has secured over $1 billion from leading investors, including Clermont Group, Coltrane, EDBI of Singapore, GIC, Indiabulls, NIBC, Toscafund, and SoftBank’s Vision Fund.

What progress has been made in the Indian market? Can you tell us a bit about your India team?

With over 450 people across offices in Gurugram and Bengaluru, India is our biggest market in terms of headcount. Our India team is critical to our operations and the ongoing success of the business. The team includes some of the best credit analysts, along with engineers and data scientists who integrate into a global product team, all focused on developing our platform.

How are you using artificial intelligence (AI) in financial services to unlock the potential in customised lending to businesses?

Our platform allows traditional financial institutions to significantly improve and accelerate their credit decisioning and monitoring capabilities. Rather than purely relying on backward-looking historical data sourced from the borrower and scenario analysis based on standard haircuts not necessarily linked to industry drivers (Level 1 and 2 analysis), OakNorth pulls in a wide range of relevant internal and third-party data sets that enhances credit analysis and creates a forward-looking view on the borrower’s business growth through benchmarking and scenario analysis (Level 3 and 4 analysis).

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

The outcome for the businesses is getting debt finance products quickly that are structured to their individual needs and will allow 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.

How data and technologies such as AI can be leveraged to enable lenders to make more informed credit decisions?

We believe that the human/computer or man and machine symbiosis 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.

How AI can fuel banks’ return to SMB lending?

The benefits to the lender from the adoption of big data, AI and machine learning technologies by banks primarily stem from what they allow them to do as a lender. Firstly, because they can analyse more complex credit situations, they are set free from the shackles of their rigid credit product suite. They should therefore be able to gain market share by having a differentiated product offering. Secondly, enhanced analysis and better use of external data should lead to better credit outcomes from a more objective, data-driven credit assessment process. Thirdly, they should see greater efficiency from a streamlined, automated process where computers do what they do well (process large amounts of data) and humans do what they do well (provide judgement and expertise).

How banks/NBFCs can leverage big data and machine learning to unlock the potential in customised loans to businesses?

Used wisely, big data alongside technologies such as machine learning can help to remove the friction that is commonly seen in customised loans to businesses. To demonstrate the validity of this model, by following this strategy of providing complex, bespoke loans to SMEs and midcap companies, OakNorth Bank has grown its lending book in the UK from zero at the start of trading in September 2015 to over £4 billion today, with yields that supported a profit of £33.9m in 2018.

What are the challenges for SMEs lending market?

If you look at the SME lending market today, a similar pattern emerges. When it comes to loans of $500k or less, big banks and platforms such as Funding Circle, Kabbage, Ant Financial, Lending Club, Iwoca, etc. offer several debt options including small general-purpose business loans, asset finance, and invoice finance. To make this commercially viable, lending is typically based on automated credit models which allow lenders to process loans quickly and efficiently.

When it comes to loans of $25m or more, banks can justify allocating significant amounts of time and resource to underwriting because the potential returns are greater. Loans that fall outside these parameters (i.e. those between $500k to $25m) however are either too large to be subject to the automated credit process that can be undertaken with smaller loans (as it is difficult from a risk perspective to justify automating this size of loan); or too small to be underwritten in the way that big banks do with large loans because the potential returns do not make it commercially viable. As a result, this segment of the market has been overlooked and underserved for decades.

Our next-generation credit platform, OakNorth, helps us solve this problem globally.

How do you deploy AI to optimise credit for customers?

We use OakNorth in the UK to do our own balance sheet lending via OakNorth Bank. The bank lends between £500k to £45m 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 to the credit committee. Our team in India comprising credit analysts, data scientists and software engineers, use the 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 reviews the paper and speaks 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 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.

What is the future of financial innovation?

Collaboration – between fintechs, among fintechs and large financial institutions, between large financial institutions and big technology companies, and possibly even between fintechs and big technology companies is the way forward. One day the fintechs can reach a scale that makes them interesting enough for big technology companies to collaborate with.

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