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.
How we’re currently using analytical Intelligence to best solve credit issues is through our tech platform, which we’re licensing out to banks across the globe. The platform works by collecting millions of data items on SMEs across various parameters, sectors, and markets, and uses machine learning algorithms to identify data that lenders need to make more informed credit decisions through detailed line item underwriting. Our team of credit analysts and data scientists manage the process, training the machine learning algorithms, so that the platform continues to evolve and get smarter. The platform therefore provides OakNorth Bank and its partners around the world with significant process efficiency gains, data-driven decision making, and smarter credit analysis and capabilities. Meanwhile, their SME clients benefit from more flexibility in the structure of the loan and the assets used to secure it, and faster decisions (both for “yes” and “no” decisions), enabling them to get back to running their business and avoid the opportunity cost of having to wait months to get an answer like they’d have to do at a traditional bank.
We have essentially found a way to leverage technology to overcome a challenge that has been facing the SME lending industry for decades. What’s more is that we’ve proven that by leveraging the right technology, you can build an incredibly strong, large SME loan book, in a very short period of time, profitably and with no defaults.
It's clear that while we may never achieve 100% automation of complex credit analysis through the use of AI and machine learning, doing so may not be a worthy goal. Rather, there are numerous advantages to be gained from the added insight and efficiency that these techniques can bring to the lender and the borrower in the process.
The benefits to the lender from the adoption of big data, AI and machine learning technologies 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).
Used wisely, these technologies can help to remove the friction that has prevented this from happening in the past. Finally, since they are no longer reacting to the market and fighting to establish a niche in a very competitive space, and are able to act at speed, they should see margins improve. 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 £3.3bn today with no defaults and yields that supported a profit of £33.9m in 2018.
Praveen Agrawal
Managing Director, India – OakNorth
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