Analysis: why is the banking industry failing on climate data?

Following the news last week that several leading US banks came up against data gaps and modelling challenges during a climate scenario analysis pilot, FStech news editor Alexandra Leonards speaks to industry experts to investigate why the banking industry is still struggling to understand climate-related financial risks.

The Federal Reserve reported last week that six of the United States’ top banks faced data gaps and modelling challenges during an exploratory climate scenario analysis pilot.

The exercise was run to help these financial institutions – which included Bank of America; Citigroup; Goldman Sachs; JPMorgan Chase; Morgan Stanley; and Wells Fargo – better understand the resiliency of their business models to climate-related financial risks.

Given that extreme climate change-related events and disruption could have a worryingly destabilising impact on the financial system, improving banks’ capacity to monitor, measure and manage these risks is becoming increasingly important. But with US banks in the Federal Reserve's pilot coming across a number of issues when trying to estimate the financial impacts of highly complex and uncertain risks over several time periods, it’s clear that the industry still has a long way to go before it can do this confidently and effectively.

"Fundamentally, predicting the impacts of climate change is literally based on long-term weather predictions, and how those will play out against a host of other factors like topography [and] demographics in specific locations,” says Adam Denninger, global insurance industry leader at Capgemini. “Just predicting the weather is hard enough; layering in the additional factors, and then trying to forecast over years or decades makes this issue incredibly complex; almost impossibly complex.”

Even the best models for climate-related risk can be questionable at an individual risk level, particularly around issues like wildfire and wind.

“It means that any institution – insurance, banking, governments, utilities, – that are trying to forecast the impacts of climate change will have inaccuracies and missed predictions, regularly,” continues Denninger. “That does not mean they are wrong to do it, in fact they have to do it: the underwriting results in insurance over the last few years prove that this is an existential issue for property and casualty insurers.”

Ultimately, he explains, all institutions that use these models will have gaps in their risk assessments because it is “just the status of the science today”.

Tej Vakta, head of sustainability solutions for financial services at Capgemini, says that while the nation’s top banks have made continuous advancements towards integrating climate science findings and application of new analyses, there are still major challenges that hinder further progress.

He explains that data gaps exist because of both non-standardised data and a lack of historical data being available. Vakta says that there’s also an urgent need for the integration and enhancement of risk methodologies that can predict long-term direct and indirect impacts, as well as intercorrelated factors.

“For instance, integrating the interplay between climate change impacts on the macroeconomy, subsequent policy decisions, and their effect on climate change itself remains a significant roadblock,” says Vakta.

Additionally, an absence of standards in data, reporting, and methodologies contributes toward the industry struggling to effectively model.

Rachel Delacour, chief executive and co-founder of sustainability data management software company Sweep agrees that assessing climate risks is no simple task and that a lack of standardised data is an ongoing issue.

“It's relatively new territory, and there is a lack of harmonised global reporting standards, all of which combine to make the challenge more complex,” explains Delacour.

But she says that this doesn’t mean it cannot be done.

“The greater the level of reporting standardisation across our economies, the easier it is to gather, disclose, and act upon the data our organisations are generating,” continues the chief executive.

When asked whether banks’ models are just not up to scratch, Capgemini’s Vakta says that while financial institutions use sophisticated models, they may not be fully equipped to handle the specific challenges of climate risk.

“When we look specifically at the dynamics generating climate scenarios, which are often across both integration and prediction for non-linear events, two significant factors limit model development: the complexity of climate change and the long-term time horizon involved,” he says. “As an example, many banks employ catastrophe models, which have their own set of challenges.”

Traditional models often struggle with adaptation and cannot fully take advantage of the volume of data available today, while catastrophe models may underestimate potential damages under future climate scenarios as the underlying assumptions in these existing models may be outdated.

Vakta says that aside from modelling challenges, the respondents in the Federal Reserve’s exercise indicated the pressing need for quality data. He says that banks need major investment in data curation – from collection to aggregation – in order to address this. This could involve the roll out of AI or data and scenario analysis tools, like satellite imagery.

Advancements in modelling are also possible through technology like AI and the expansion of data analytics, while machine learning and using a range of innovative data sources – like remote sensing data, satellite imagery, and geographic information systems (GIS) – could also help to solve the banks’ data problem.

“Advancements in modelling can be realised through interdisciplinary teams, AI, and expansion of data analytics,” he continues. “This could come alongside strategic partnerships with climate scientists, engagement in open-source initiatives like OS-Climate and leveraging successes from other industries.”

Vakta says it is crucial that modelling techniques are further improved by integrating components that enable automatic updates of assumptions based on new data and climate predictions.

“Financial institutions are the bedrock of our economy and we have seen in recent years the havoc that can be wreaked across the world when these institutions succumb to avoidable risks such as the 2008 subprime crisis,” says Sweep’s Rachel Delcour. “In the same way that it could have been possible to foresee and avert the subprime crisis, it is possible now to forecast climate change related risks, and mitigate them.”

She says that technology will of course be part of the answer, with the Bank for International Settlements recently having had positive results with an AI-powered analysis of climate related risks in the financial system.

The impact of climate-related risks are already clear, with US government figures revealing that last year the country faced 28 separate weather or climate-related disasters, each resulting in at least $1 billion in damages. Banks must improve their resiliency to these growing risks, or the world’s financial system could face detrimental consequences in the future.

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