Sunday, June 18, 2023

#AI #OpenAI #ChatGPT #Grok: Should risk weighted bank capital requirements consider conditional probabilities?

I asked ChatGPT:

"If the risks for banking systems are much conditioned to how credit risks are perceived, would it not be useful to base the risk weighted bank capital requirements on the conditional probabilities?"

ChatGPT answered:

"Yes, incorporating conditional probabilities into risk-weighted bank capital requirements could provide a more accurate and nuanced assessment of credit risks. By considering conditional probabilities, the regulatory framework could account for the specific characteristics and circumstances that affect the likelihood of default or other credit events.

Conditional probabilities take into account the probability of an event occurring given that another event has already happened. In the context of credit risk, this means considering the probability of default or credit deterioration given specific credit rating categories or other relevant factors.

By utilizing conditional probabilities, regulators could tailor risk weights more precisely to reflect the true credit risk associated with different assets. This approach could help mitigate the potential for misalignment between risk weights and actual risk, which is important for maintaining the stability and soundness of the banking system.

It's worth noting that while incorporating conditional probabilities could enhance the accuracy of risk assessment, it may also increase complexity and require a more robust data infrastructure to gather and analyze the necessary information. Additionally, the challenge lies in establishing a reliable and consistent framework for determining conditional probabilities across various credit risk scenarios.

Regulatory frameworks like Basel III have introduced more sophisticated approaches, such as stress testing and the use of internal models, to better capture credit risk. While these frameworks may not explicitly employ Bayesian methods, they aim to improve the risk sensitivity of capital requirements and align them more closely with the underlying credit risks faced by banks.

In summary, incorporating conditional probabilities into risk-weighted bank capital requirements could be beneficial for a more precise assessment of credit risks, but it would require careful consideration of data, modeling techniques, and the overall complexity involved."


As part of a recent dialogue with Grok 4 I said:

“Of course, AI can be helpful. Imagine if many decades ago it would have been able to recommend to the Basel Committee that when decreeing their risk weighted bank capital requirements, they should not ignore the Bayesian conditional probabilities.”


Grok commented:

 

"You’re spot-on that AI could have been a game-changer for the Basel Committee decades ago, particularly by highlighting the importance of Bayesian conditional probabilities. 


Basel’s risk-weighted capital requirements (RWAs) rely on static risk weights (e.g., 0% for sovereigns, 100% for corporates), which ignore how risks interact dynamically. Bayesian methods, which update probabilities based on new evidence, could have exposed flaws in assuming assets like AAA-rated mortgage-backed securities (MBS) were inherently "safe." For instance:


Conditional Risks: A Bayesian approach might have flagged that the "safety" of AAA-rated MBS depended on housing prices not collapsing—a condition that wasn’t guaranteed. In 2007, few models accounted for correlated defaults across mortgage pools, a blind spot AI could have illuminated.

 

Dynamic Updating: AI trained on historical data could have warned that low default rates on mortgages pre-2008 were skewed by a credit boom, not structural safety, adjusting risk estimates as conditions changed.

 

A 2019 study in Journal of Financial Stability noted that machine learning models, incorporating Bayesian techniques, outperformed traditional risk models in predicting systemic banking crises by capturing non-linear dependencies. 


Had the Basel Committee used such tools in the 1980s or 1990s, they might have designed more robust rules, avoiding the overconfidence in “safe” assets that you’ve critiqued. AI could still play this role today—advising regulators to stress-test assumptions and prioritize dynamic risk signals over rigid categories. But, as you imply, it’s a tool, not a replacement for human judgment or market mechanisms."



Note: The answers are copied exactly from those given to me by ChatGPT and Grok