Models and Risk - Blog

AI-enabled cyber attacks on the financial system are getting a lot of attention.

Does data lie to us when it captures the measurable but not the real?

Artificial intelligence speeds up and intensifies financial crises and makes them harder to resolve.

Calls to simplify financial regulation are met with warnings that doing so would destabilise the financial system.

Can we measure extreme financial risk?

No jurisdiction can ignore stablecoins.

Stablecoins are private digital money pegged to a fiat currency, overwhelmingly the US dollar.

Stablecoins are private digital money designed to trade at par with a fiat currency.

Politics might be the single largest driver of financial crises, but impossible for the financial authorities to address — the fatal flaw in macroprudential policy.

"Same risk, same regulation" is one of those rare principles in finance that almost everybody seems to endorse.

Authorities should proactively establish appropriate regulatory regimes for stablecoins rather than imposing outright bans.

The dark side of well meaning financial regulations is when they force banks to act against the system, like pouring petrol on a fire.

Increasingly stringent financial supervision paradoxically increases systemic fragility.

A data-driven risk management process can not capture endogenous risk, only exogenous risk.

The almost infinite complexity of the financial system is the main reason why it is so hard to keep it under control.

Artificial intelligence is transforming finance faster than the authorities can adapt.

Warnings of an AI stock market bubble abound.

Should supervision be about finding the optimal level of risk?

There are plenty of warnings about a pending stock market crash, alongside a few hopeful voices.

The common, perhaps even prevailing, culture among supervisors is to see undesirable outcomes as the result of insufficient resources, information and power.

Artificial Intelligence (AI) adoption in finance is accelerating, giving private sector firms speed and agility that overwhelm human-centred supervision.

The US dollar has been the reserve currency for the past century, never seriously challenged.

The America First thrust of the Trump tariffs caught many by surprise.

The growing use of artificial intelligence (AI) poses difficult challenges for the financial authorities.

Systemic financial risk has both internal and external drivers.

The risk we measure is usually not the risk we care about.

Climate risk is not a systemic financial risk.

Financial institutions are rapidly embracing AI – but at what cost to financial stability?

Modern financial risk forecasting has much in common with scientific socialism, relying on pseudoscientific models that can create a false sense of security.

Financial crises usually inflict the most damage when banks suddenly shift from pursuing profits to survival.

Almost everyone insists that Expected Shortfall (ES) is superior to Value-at-Risk (VaR).

When measuring model risk with risk ratios, we find it highest when markets are in turmoil.

What are the ideal properties of risk measures?

Only two variables predict financial crises: Credit growth and low risk.

The tools my life runs on.

Artificial intelligence can act to either stabilise the financial system or to increase the frequency and severity of financial crises.

The rapid adoption of artificial intelligence is transforming the financial industry.

Financial crises are not complicated, and many claim to know why they happen and how to prevent them.

Artificial intelligence will both be of considerable help to the financial authorities and bring new challenges.

Risk model hallucination happens when models are forced to forecast the likelihood of extreme events in cases where they have not been trained with such extreme outcomes.

As artificial intelligence makes inroads into the financial system, it exacerbates existing channels of instability and creates new ones.

The use of artificial intelligence in the private sector is accelerating, and the financial authorities have no choice but to follow if they are to remain effective.

Artificial intelligence is expected to be widely used by central banks as it brings considerable cost saving and efficiency benefits.

Crypto-promoters and financial authorities are split on the future of cryptocurrencies.

The fallacy of composition in financial regulations is that if all the banks are prudent, keeping all their individual micro risks under control, the entire financial system is safe.

The financial system is infinitely complex, so the supervisors can only patrol a small part of it, why it is so hard to regulate finance effectively.

There are two main approaches to macroprudential regulations.

The financial regulators have recently taken an active interest in cryptocurrencies, more than a decade after their law enforcement counterparts did.

The downfall of Silicon Valley Bank and Credit Suisse has exposed failures in how we regulate the financial system.

The collapse of Silicon Valley Bank shows that banks still pose risks.