The changing shape of risk

In increasingly volatile and interconnected markets, risk can no longer be navigated in silos. Whit McGraw, head of credit and risk solutions at S&P Global Market Intelligence, reveals how firms are adjusting their strategies and capabilities to embrace a more holistic view of risk

Geopolitical tensions, inflation, regulatory change and supply chain disruption have loomed large over markets in recent months. How do you expect the outlook to evolve in 2025?

Whit McGraw: I see more of the same. Specific risk factors may increase or decrease. For example, most economists see inflation continuing to moderate but, at the macro level, the number of different risk factors and their integrated nature will very much remain the same.

The external environment for all businesses is becoming increasingly complex and multifaceted, and that is now the new normal. Notably, in the past four years, I have seen an increase in extreme and ‘rare’ shocks – such as the Covid-19 pandemic, oil prices, regional wars, physical risk events and cyber disruptions.

There is the potential for an onset of instabilities and tipping points in 2025, with ripple effects and increased volatility across global markets.

What are the most significant challenges that capital markets firms face managing risk in today’s environment?

Whit McGraw: The number of different risk factors a financial institution must traverse is daunting. Then, adding the fact that many of these risk factors are interconnected, the complexity only increases.

So the expanse and interconnected nature of risk factors are what makes operating in this environment so difficult – especially compared to just 10 years ago.

On top, you have ever-changing regulation, data privacy challenges and a period of hyper-technological advancement – namely generative artificial intelligence (GenAI) – and you quickly realise the criticality of having robust, effective risk management strategies from the board level down.

New risks, such as cyber threats and climate change, have become more significant factors in managing credit risk. To what extent are firms incorporating these factors in their assessments?

Whit McGraw: This goes back to the trend we are seeing around integrated risk. For example, if you are a portfolio manager at a bank, understanding financial and credit risk is core but, increasingly, you will also want to understand how other risk types, such as climate-related impacts, might affect your portfolio. The same applies to cyber.

An even better example might be a supply chain risk officer who not only wants to understand credit risk, but also climate, cyber, geopolitical risk, and so on. If a company within your portfolio or supply chain experiences a cyber attack, it will undoubtedly have an impact on the credit profile of that entity. Tools, techniques and risk frameworks are evolving in this direction, as is the need for insightful data to accelerate decision-making.  

Regulatory initiatives such as Basel III are mandating a more granular approach to risk management. How are firms responding to the data challenge? How can they future-proof their investment in data? 

Whit McGraw: In discussions with market participants and chief risk officers, I see increased interest from financial institutions in using or collecting alternative datasets from specialised data providers to combine or standardise their own internal datasets and make them available across multiple, often disconnected, databases and systems.

It is critical for financial institutions to tap into high-quality, standardised datasets with good coverage across all regions, provided by reliable global providers. Where data is missing or remains incomplete, it is good practice to leverage solid benchmarks constructed from large datasets using industry best practices.

Even for institutions that already hold large amounts of data, it is useful to run comparative analyses on external benchmarks to ensure risk management incorporates a multidimensional perspective.

What trends are you seeing in the way risk teams are adapting their structures, strategies, tools and processes to better manage these risks?

Whit McGraw: Financial institutions, but also non-financial corporations at large, are recognising the importance of leveraging automated systems for risk analysis and management purposes, making decisions in a timely manner vis-à-vis more frequent and extreme shocks experienced in recent years.

I also see a keen interest in developing predictive analytics that enables business and risk management leaders to anticipate risks before they materialise – allowing a firm to activate remediation plans before it is too late – and scenario tools to brace for worst-case situations, navigating market volatility, updating risk limits or leveraging insurance policies based on the chief risk officer’s risk appetite level.

What potential do AI and machine learning bring in this regard?

Whit McGraw: AI and machine learning techniques have the potential, already realised in certain instances, to facilitate the generation of meaningful insights from a multitude of datasets, enabling firms to achieve cost efficiencies and redeploy personnel into other strategic departments/functions. Additionally, these tools can help capture interdependencies among risks and reveal patterns that are not evident at first glance.

How do you expect S&P Global Market Intelligence’s offering to evolve in the coming months?

Whit McGraw: Our offering is evolving to be able to provide insights across all risk domains – credit, market, operational – with a particular focus in areas related to AI, private markets and private credit, climate and supply chains, as well as third-party risk management.

On the AI front, S&P Global Market Intelligence is spearheading a number of initiatives that will see an increased use of AI in general, and GenAI in particular. Specifically, within our credit and risk solutions business, GenAI will provide risk practitioners with a contextualised narrative on the analytical outputs produced by our risk solutions models and S&P Global Ratings’ research, adding transparency and clarity, and enabling clients to make better and more timely decisions. GenAI will also be considered to accelerate calculations for market risk purposes, achieving faster performance than via current industry-standard approaches.

On the private markets side, we are looking to serve an ever-growing demand by achieving ‘universal’ coverage of private companies worldwide, introducing benchmark loan curves and collaborating with S&P Dow Jones Indices to explore new indexes and market benchmarks.

For climate, we are leveraging our internal climate- and energy-related datasets in our scenario tool – Climate Credit Analytics, developed in collaboration with Oliver Wyman – to provide insights into equity and bond market risk under multiple scenarios.

We are seeing an increasing focus on third-party risk across all industries. Our solutions are evolving to address client demand for integrated risk analytics, combining cyber, geopolitical and other factors to provide insight and drive rapid decision-making.  

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