RiskMinds 2021: “What keeps CROs awake at night?”

In December 2021, after two years absence, the two largest gatherings in Financial Risk and Quant Finance: RiskMinds 2021 and QuantMinds 2021 – took place, this time in Barcelona.

RiskMinds is where CROs of all large (and numerous smaller) banks meet – normally annually. The event is filled with panel discussions of prominent risk professionals, who exchange ideas and opinions about the most pressing risk issues, latest developments in risk and regulation and the ways of tackling these issues. These informative panels are interspersed with technical presentations, regulatory workshops, and fascinating risk-related speeches from risk gurus outside finance industry: hostage negotiators, spy chiefs, diplomats, professional gamblers.

QuantMinds is the most prestigious event in quantitative finance. Prominent as well as young quant professionals present the newest developments in derivatives, risk models, quant investing and applications of modern technologies to finance: ranging from AI and machine learning to quantum computing. This gathering is an endless source of inspiration and innovation. Although quite technical, the sessions give a good impression of frontier research and latest developments in quant finance.

So what innovative topics were at the frontier of QuantMinds this year? I will answer that question in my next article, but now I will summarize the main takeaways from RiskMinds 2021: “What keeps CROs awake at night?”

Three main themes, that clearly dominated the financial risk agenda, emerged during the RiskMinds conference: climate risk, credit risk and model risk. However, when asked, what keeps them awake at night, the CROs answers were: climate risk, closely followed by increasing regulatory pressure. All other typical CRO worries: such as cybersecurity or operational risk, have been upstaged by these two concerns. But since it does not make sense to guess what sort of regulation will hit them next, the risk professionals preferred to focus on climate risk, and more tangible issues such as post-COVID developments in credit risk and model risk.

For climate risk, after countless panels and presentations, it became clear nobody had any practical or relevant ideas of how to deal with this kind of risk or the associated stress testing. There was a lot of lip-service devoted to climate risk, as well as good intentions of the kind “we should focus on it and deal with it”, but unfortunately not much substance. One presentation stood out as an exception to this fog of non-specific blither: that of Alexey Bogatov, Head of Enterprise Risk Management at Sberbank, the largest Russian bank. He described how Sberbank deals with climate risk, and presented a fully developed and integrated approach, which starts with realistic scenarios, provided by expert bodies such as the IPCC, and continues to the macroeconomic consequences of two key aspects of climate risk: transitional and physical risks. From these macro consequences, the bank has deduced some broad consequences for their credit portfolios, financing and growth opportunities, as well as other wider-ranging consequences. It was surprising to see how they could associate well-considered and quantifiable costs of various types of transitions to greener economy, on a macro level; and do this as one might expect from a central bank or governmental economic advisory body. The narrative presented and logical chain of consequences of this narrative clearly impressed the audience. Especially remarkable was the choice not to descend into micro-consequences of climate risk – which are impossible to envisage and quantify – but focus instead on the bigger picture.

A lot of airtime at the conference was devoted to post-pandemic developments in credit, and more generally, to banks’ resilience in a post-pandemic world. Here, the participants were aligned in their opinion: the consensus was that a wave of bankruptcies and defaults is about to hit (as soon as government support to businesses ends), and the question is only when this will happen and how big this wave will be: not whether it will happen or not. The only dissenting voice was by the CRO of a well-known online bank, who remarked that they observed how well their clients adapted during the pandemic (e.g., providing e-commerce – related services: such as takeaway meals or online shopping). Clearly, the loan portfolio of such a tech-based bank is atypical, as their clients are probably more internet-savvy and hence were better prepared to be flexible during lockdowns and restrictions. But the rest of CROs agreed there would be a deluge of defaults soon; and they seemed surprisingly calm about this – presumably because the capitalization of banks improved so dramatically before the coronavirus pandemic, that they feel they can deal with this upcoming wave of defaults without too much trouble. Moreover, they gave the impression that they have a pretty good idea where in their loan portfolios the main damage will occur, and are prepared for this. However, this attitude suggested to me a false sense of security: as the second-order effects of this default wave (such as bankrupt businesses defaulting on their mortgages) were not discussed at all. There were worries about potential supply chain disruptions and their consequences for credit – which is arguably also a second-order effect – and this disruption was clearly seen as a potential danger to the economy as a whole and to credit portfolios.

Finally, the ongoing issue of model risk – and especially of how risk models have performed during the COVID pandemic – was an interesting topic for several panel discussions and presentations. A remarkable presentation on this topic came, coincidentally, also from Sberbank: their Head of Model Validation, Roman Tikhonov, presented a well-developed and comprehensive system for bank-wide ongoing model monitoring (for all models within the bank). This was a learning experience for others, who struggle with model inventory, as well as with keeping track of their models’ performance in real time. Other speakers repeatedly stressed that the COVID pandemic has shown banks should not completely rely on their models and should “wean” themselves from dependence on models, as many of those models have derailed significantly over the past 1.5 years. Especially in the light of these remarks, Sberbank’s concrete plan of how to continuously monitor and manage model risk could serve as an example.

In my next article, I will take you through the most innovative developments in quant finance and modelling discussed at QuantMinds 2021, and will examine what these developments could mean for financial institutions