Risk Insights

For much of the past decade, the credit asset class benefited from a highly supportive macro backdrop. Rising base rates, coupled with floating-rate structures, provided a largely mechanical uplift to income. Returns were often driven as much by macro conditions as by credit selection. Recent geopolitical developments, notably the Iran conflict, have complicated the outlook for inflation and monetary policy.

dataAs jurisdictions around the world move to regulate artificial intelligence and the industry’s biggest names try to have their say, a new venture is offering to bring quantitative objectivity to organizations’ governance of the technology. The AIQ Score, administered by AIQA Global, takes into account five aspects of AI implementation: strategic alignment; oversight and accountability; technical robustness; responsible AI and compliance; and adaptability and education.

Managing model risk and adhering to institutional compliance expectations at scale requires extensive effort and exceptional attention to detail. However, even the most diligent risk managers are frequently restricted by continuous changes to regulations and market demands, and limited by manual and outdated process controls.

In my 30 years in the payments industry, I can’t count how many times I’ve been in the middle of “modernizing” – aka fixing – a process, only to be called into a boss’ office to be told: “Stop what you’re doing, it’s time to bolt on something new.” It happens time and again across financial services. The new shiny thing or the latest risk of becoming irrelevant in the market will always capture our attention. Yes, we must stay at the forefront of technology and customer demand.

Every cascade that risk management has successfully governed has had a traceable entry point. In the CrowdStrike outage, it was the endpoint update mechanism. The MOVEit breach entered through a transfer protocol vulnerability, the Bybit theft through a compromised signing interface. This traceability is the premise on which enterprise risk management (ERM) frameworks are built. AI breaks that assumption by making attack surfaces non-enumerable.

The future of payments and financial markets is taking shape – and has to some extent already arrived – in the form of cryptocurrencies, stablecoins, and a variety of instruments in tokenized form including deposits, equities and other so-called real-world assets. The origin of bitcoin, after a paper published in 2008, was a big bang that continues to expand and proliferate, and that traditional market structures and regulatory frameworks are still coming to grips with.

From the inception of financial engineering, it took the industry decades to integrate value-at-risk (VaR), Greeks, and copulas into its standard business practice. Artificial intelligence is currently in such an introductory phase. Acknowledging this parallel can be challenging, as it subverts the conventional idea of risk management. The Sandbox Problem Shake a sandbox repeatedly, and certain light objects rise to the top.

The April 7 introduction of Claude Mythos was no ordinary software release.

Institutional investors, banks and insurers rely on physical climate risk data to understand how changing climate conditions will affect the assets underlying their portfolios. The problem is that physical climate risk data vendors, when evaluating the exact same asset, frequently reach different conclusions about which hazards pose the greatest risk, how severe those risks are, and sometimes whether meaningful risk exists at all.

For decades, Enterprise Risk Management (ERM) operated under the rule of law with a stable domestic sovereign and a judiciary that operated as a predictable referee. While geopolitical risk frameworks carefully mapped the predatory behaviors of foreign regimes, domestic stability was assumed. Today, that assumption is dead.