The fintech scene in India today is incredible, says Praveen Agarwal, Managing Director, OakNorth India in an interview with CIO Axis.
CIO Axis: What is the vision of OakNorth? How you plan to align your India operations with the same?
Praveen Agarwal: Our vision is to redefine lending to small and medium-sized businesses globally using our next-generation credit platform, OakNorth. Our India operations plays a vital role in this as it is our largest team by headcount.
CIO Axis: What is the current landscape of SME credit business in India? What are the challenges for SMEs loan disbursement process?
Praveen Agarwal: If you look at the SME lending market around the world, 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 however (i.e. those between $500k to $25m), 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 don’t 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, is how we’re solving this problem globally, but we don’t currently license it to any banks in India and the only market where we lend off of our own balance sheet is in the UK.”
CIO Axis: Are Indian lenders using latest technologies, such as artificial intelligence to make more informed credit decisions?
Praveen Agarwal: Across India and around the world, the fintech sector is revolutionising financial services and democratising industries that have remained unchanged and unchallenged for decades. Fintech businesses are leveraging new technologies such as machine learning and the cloud to create products and services that have the potential to transform the lives of billions of people globally. By 2020, the global fintech sector is expected to be worth over $45bn.
Looking at the fintech scene in India today, it’s incredible. The innovation and career opportunities it is creating a as a result is unbelievable. According to EY’s ‘FinTech Adoption Index 2017’, India now sits second in the world, only behind China, in the adoption of fintech services across various industry sectors. In fact, over half of Indian consumers claim to have used more than two fintech products in the last six months. The analysis also indicates that India will lead the global fintech sector in years to come, with several factors driving this trend. These include high demand from Indian consumers for new and personalised digital experiences, as well as the positive results following the completion of India’s new architecture for financial services that is inspiring further innovation in fintech.
In terms of lending specifically, we don’t know or keep track of what other lenders are doing. We just focus on what we’re doing and try to continue delivering the best outcomes for our customers.
CIO Axis: How can banks/NBFCs leverage big data tools, data analytics and machine learning to improve the lending decision and better assess the creditworthiness of the borrowers?
Praveen Agarwal: 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.
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% 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.
CIO Axis: How can AI fuel banks’ return to SMB lending? How do you deploy AI to optimise credit for customers?
Praveen Agarwal: 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).
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
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.
CIO Axis: What is the future of financial innovation? What is your business model and what is your growth plan?
Praveen Agarwal: Collaboration – between fintechs, between fintechs and large financial institutions, between large financial institutions and big tech, and possibly even between fintechs and big tech one day if the fintechs can reach a scale that makes them interesting enough for big tech to collaborate with.”