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Assessing the credit capacity of Small Businesses based on climate change factors

Assessing the credit capacity of Small Businesses based on climate change factors 3

Assessing the credit capacity of Small Businesses based on climate change factors 4By Mark Somers, Director of Research at credit risk analytics firm, 4most

Climate change represents a material, but often hidden risk, to the business models of many Small and Medium-sized Enterprises (SMEs). At the same time, these organisations are critical to achieving the UK’s carbon targets. A recent British Business Bank survey highlighted that circa. 50% of UK business emissions come from the SME sector; however only 11% have any measure of their carbon footprint (British Chambers of Commerce survey). Whilst Climate and Environmental Risk (CER) assessment and implications for credit for larger corporates is gathering momentum, there remains major difficulties in securing suitable information relating to SME’s.

Traditional mass market credit risk approaches are not fit for purpose in assessing the potential impact of climate change on SME borrowers, because of the absence of historical data to calibrate statistical models. Manual underwriting-based approaches are also challenging because there is no agreed methodology and a lack of skilled underwriters to assess climate risks. Overall, there is a lack of focus from financial institutions in assessing the environmental sustainability and subsequent credit quality of SMEs. This is despite their material contribution to UK business emissions and relatively more exposed position to climate risks, compared to larger organisations who have the capacity to invest and to mitigate the risks.

Climate risks can be categorised as principally being either physical – i.e., the direct effect of a hotter, more violent climate, or alternatively, transitional risks – those effects driven by changes in the economy required to make them environmentally sustainable and limit temperature increases to survivable levels. These risks are difficult to quantify in the case of the majority of SME credit, because it is expected that the timescale for the risk to materialise is typically longer than the contractual length of most SME loans.  The timescale mismatch, however, does not mean that the economies and therefore SME lending, is not exposed to new and evolving risks. Events are linked in unexpected and non-linear ways that result in once unlikely events, becoming increasingly likely even when the “average” impact is still some time off.

It is often the case that the specific mechanism for a risk to emerge is too convoluted to be able to clearly foretell. The potential for that risk to emerge is however less difficult to identify. Furthermore, the sectors and individual firms most exposed and the potential financial impacts can also be assessed in a systematic way.  To do this requires us to reconsider how SME credit decisions are assessed.

Back in the late 1970’s, in the absence of the wealth of the systematically captured data available today, and the compute power to process it, underwriters developed the suitably drinks-based acronym CAMPARI (Customer, Ability, Means, Purpose, Amount, Repayment, Insurance). This acronym helped underwriters structure the narrative investment case for loan applications and decide which ones to grant and which to decline.  This approach was limited by its subjectivity and the inability to provide risked based loan terms or finely control the profile of lending accepted. For example, you cannot easily ask an individual underwriter who handles ten loans per month to reduce the marginal risk accepted by 5%.

In more recent times banks have moved on to favour more quantitative based measures such as various leverage ratios, debt service cover calculations and credit models. These rely on accurate financial data capture and the ability to assess statistically the performance of similar loans over a reasonably long time.  Implicit is an assumption that the past is, at least in some ways, useful at identifying the behaviour to be expected in the future even without fully understanding the causal mechanisms. It is unclear that any of these metrics or models will be directly relevant in ranking borrowers for effects of climate risk.

Climate change risk assessment at least in part, turns the clock back – specifically, we cannot be confident that historical behaviours are a predictor of climate risks in the future.  Instead, we need to revert to causal understanding of the likely outcomes; higher costs for carbon intensive activity, greater requirements for fuel efficiency, more local production, greater risk of extreme weather to name a few. There are four main approaches to this climate credit risk assessment currently used:

Individual Qualitative Assessment – this is used by a number of global banks to build a detailed understanding of the individual borrower and their climate mitigation strategy. It is highly resource intensive, subjective and not necessarily comparable between borrowers or lenders.

Market Price Implied – this has been used by Bank de France and the New York Fed. It assumes the markets have rationally factored in climate risks into asset prices. This is dynamic and can be applied to publicly quoted firms – the concern is that markets are not fully able to reflect the risks and are influenced by shorter term priorities and therefore may under-recognise the costs of climate change.

Balance Sheet Carbon Cost – this is the approach proposed by Moody’s amongst others. It models the cost of increased carbon emission on company balance sheets and cash flows. While attractive in principle, there is weak evidence for the indirect effects of carbon costs on many firm specific profit and cost drivers. To capture these indirect effects requires detailed and judgmental estimates that are difficult to ground in experience.

Climate Rating Approach – This approach is favoured by the ECB (and 4most). It uses sector level models and flexes to reflect various standard climate scenarios. In some implementations it splits the problem into macro-economic effects on the sector and the judgmental idiosyncratic effects based on the individual borrower’s climate strategy and current emissions.

One of the interesting possibilities in assessing a borrower’s climate strategy is that more advanced borrowers from a climate perspective are potentially a good lead indicator of lower credit risk generally. This hypothesis is being tested actively and could create an economic mechanism for rewarding early adaptation to climate risks even before the direct effects of climate change are commercially important.

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