Calling all lenders: it’s time level-up your risk management game

Today’s world demands a dynamic approach to risk management.  Not only are lenders required to prepare for economic downturn and a potential recession, but also the impacts of climate change and political uncertainty on the horizon. It’s now more important than ever before to enhance approaches to risk models, scenario analysis and stress testing by embracing data-driven insights to stay on top of global change.  Hugh Shannon, Head of Sales and Customer Success at OakNorth explores how lenders can move towards a data-driven approach to risk, shares his top recommendations on navigating climate risk and much more in this exclusive interview.

1. How can commercial banks better assess credit risk to reduce uncertainty in a downturn?

The last time there was a deterioration in the credit cycle was 14 years ago, during the ‘08 financial crisis, so it’s been an exceptionally long bull market considering the average economic cycle in the US has lasted roughly 5.5 years since 1950.

This crisis accelerated the use of stress testing by regulators with the largest banks needing to conduct supervisory stress tests on an annual basis. This stress testing, when carried out at an individual borrower level, takes a fundamental approach – i.e. a credit analyst constructs a financial model (usually using Microsoft Excel) to simulate the cash flow, balance sheet, and income statement of the business. They then project this forward for the lifetime of the loan and use assumptions to ‘sensitize’ or stress this model to observe the performance of the business under adverse circumstances. This modelling is “augmented” by peer group analysis at a macro and sector level (typically looking at a dozen or so sectors), where a prospective borrower is compared with other similar businesses in order to establish reasonable expectations for future performance.

The issues with this approach are two-fold: firstly, it assumes that tomorrow will be a lot like yesterday which is unhelpful given every recession is different. And secondly, most businesses are more or less alike, which misses their unique differences. In a recessionary scenario where consumer spending is tightened, the experience of a budget downtown hotel for example will likely be very different from a luxury resort. The same can be said for food & drink, retail businesses, etc.

2. What’s the issue with the traditional approach to scenario analysis? 

As demonstrated by the COVID-19 pandemic, when it comes to adverse events, the traditional approach to commercial lending – using historical data, financial modelling of a base case, worst case and best-case scenario, and conducting annual reviews – is an approach that is not fit for purpose. In uneventful times, these models are fine. However, for unprecedented events such as the pandemic, the traditional models proved useless as historical correlations were broken; employing the traditional look-back approach was meaningless.

Commercial lenders need to be able to run a “bottoms-up” analysis of their loan books, assigning each business a vulnerability rating based on a subsector-specific, forward-looking credit scenario taking liquidity, debt capacity and profitability into account. This more dynamic view of risk is still valuable in a more stable economy because we can update risk inside a lender’s review cycles, allowing them to take a critical view of their loan book and maintain constant focus on the items of highest impact.

The realization that many industries experienced through COVID-19 is the same: we can’t predict the future but must be better prepared for the unknown and reduce risks across our businesses with an ability to adapt quickly with data-driven decision making. In doing so, banks will identify opportunities to lend faster, smarter and more to businesses.

3. How should commercial lenders be thinking about climate risk?

Climate change is a grave global issue that impacts us all and brings with it unique challenges for commercial lenders. These include:

• Data: either lack of data, or data that exists in unstructured form
• Models: the risk models and frameworks banks have used in the past aren’t fit for purpose
• Scenarios: explain-ability and defensibility of climate scenarios and underlying drivers
• Lack of consistency: the need to adopt commonly agreed frameworks
• Expertise: building out internal climate governance, together with external partners

Climate change is a grave global issue that impacts us all and brings with it unique challenges for commercial lenders.

In terms of addressing these challenges, we recommend banks follow the below guidelines:

1. Data and scenario analysis:

• In order to effectively examine the effects of climate change at the counter-party and exposure level, firms will need to gather and structure data specific to the operations of borrowers in specific industries.
• Banks will also need to consider a broad range of scenarios with sufficient granularity to enable them to adequately assess the risks of meeting their risk management objectives and wider climate change targets.

2. Develop a loan-level understanding of how risk cascades down the value chain:

• The US automotive industry is the perfect example…there are approx. 18,000 new-car dealerships selling cars with internal combustion engines, however, how will these thousands of businesses be impacted if most new car sales evolve to electric?
• Banks need to have a borrower (or asset) level understanding of their portfolio and take a forward-look view around how these businesses could be impacted.

3. The only way to develop a loan-level understanding is by having the right people, processes, and technologies in place:

• Front-line teams are the ones dealing with bank customers, and who will need to have conversations with them about transition risk and the changes they can and should be making to their business models to address these.
• Banks should be coaching their front line to have transition-related conversations with clients, providing them with the right questions to ask for different industries and sub-sectors, and using the data from these discussions as one of several inputs to help inform decision-making.