When it comes to commercial lending, a solid approach to credit risk management is essential. Accurately and efficiently determining the creditworthiness of new or returning borrowers will help traditional lenders and new lenders increase the amount they lend to businesses, in addition to also reducing the risk of late payment or default.
In our experience, most banks are limited to using traditional datasets, such as historical financial data, personal experience of a particular sub-sector and, above all, no real-time data to provide forward-looking information. However, with more and better data, commercial lenders can find the “right path to yes,” expanding the range of businesses they lend to and uncovering new opportunities with their existing customers. With an increased level of understanding, relationship managers and analysts working in banks will undoubtedly gain the confidence to lend to unfamiliar industries, backed by industry insights and peer comparisons.
Adopt a forward-looking vision
A typical high-growth company is growing 20% year over year, so the difference between its last 12 months and its next 12 months is about 40%. So, if a lender lends to that company only based on its historical data or past performance, they are not lending to it based on what is necessary to support its current or future growth trajectory. It’s akin to driving based only on what you can see in the rearview mirror instead of driving looking ahead through the windshield.
It’s so important for lenders to use data and insights to get a clear picture of a company’s future growth potential and direction, rather than just relying on its track record. Unfortunately, many lenders still view high growth as high risk, and because they are unable to develop a reliable forward-looking view of a business, they cannot lend them comfortable loans.
Data and Analytics Capabilities
At OakNorth, we strongly believe that traditional lenders can leverage data and credit science to enhance humans rather than replace them. This hybrid approach is a pragmatic compromise where the computers perform various tasks to enable the credit analyst to be more efficient, but the analyst remains in control and is able to train the models and direct and shape the end results to make sure they are consistent and cohesive. understandable.
Due to the complexity of the space, we do not believe full automation is a desirable end goal and instead aim for around 80% automation, with a human analyst still involved in the process. This critically allows human judgment to always have an influence on the result and helps ensure the understandability of the results.
Additionally, when it comes to business loans, traditional lenders must assess a company’s ability to sustain a certain level of debt and repay the loans. This is where data science comes in. The only way for them to effectively assess trade credit risk is to use multiple sources of data – including unconventional or previously unavailable data – rather than relying solely on what they have used in the past. At OakNorth, we apply data science techniques to create unique models that provide the granular level of analysis for each borrower. By combining borrower-provided data with our extensive repository of external data, we are able to deepen ad hoc analysis and monitoring.
Develop a granular, loan-level understanding of a business
Traditional lenders tend to group all businesses into a dozen categories – for example, all restaurants, hotels, bars, leisure facilities, etc. fall under the category “hospitality and leisure”. There are a few problems with this approach – first, it ignores the unique differences between companies within the same industry, and second, human biases can mean that some relationship managers or teams are reluctant to lend to a specific company because ‘it comes from an industry with which they have had a negative credit experience in the past. This question has come to light over the past couple of years with COVID, as companies in similar industries or subsectors have had very different experiences at different stages of the pandemic. Take for example a golf club and an indoor climbing center in the same city – both would be classified as “physical leisure activities”, but their experiences over the past two years will have varied considerably. The golf club – which takes place outdoors, is played over huge social distances and where players bring their own clubs – will probably have been able to reopen and start trading much sooner than the climbing center in hall, which, being indoors, will have less fresh air, and which will see climbers touching the same holds in quick succession.
Developing a granular understanding of a lending-level business will allow them to structure a facility tailored to that business’s needs.
Perform event-based scenario analysis
As demonstrated by the COVID-19 pandemic, when it comes to adverse events, the traditional approach to commercial lending – using historical data, base-case, worst-case financial modeling best-case scenario, and conducting annual reviews – is an approach that is not fit for purpose. In calm weather, these models are fine. However, for unprecedented events such as the pandemic, traditional models have proven useless as historical correlations have broken down; using the traditional retrospective approach made no sense.
Commercial lenders must be able to perform a “bottom-up” analysis of their loan portfolios, assigning each company a vulnerability score based on a subsector-specific forward-looking credit scenario that takes into account liquidity, debt capacity and profitability. This more dynamic view of risk is always valuable in a more stable economy, as we can update risk within a lender’s review cycles, allowing them to critically view their loan portfolio and to remain constantly focused on the most impactful elements.
The realization that many industries have experienced through COVID-19 is the same: we cannot predict the future, but we need to be better prepared for the unknown and reduce risk across all of our businesses with an ability to adapt quickly through data-driven decision-making. In doing so, banks will identify opportunities to lend faster, smarter and more to businesses.
The opinions expressed above are those of the author.
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