Regulatory Capital and Catastrophe Risk

Date : Tags : , , , , , ,
A #regulatory #reform that imposes greater regulatory #capital #costs for #insurers to provide property coverage in catastrophe-prone areas results in price increases, though the magnitude of the increases is restrained due to #insurance pricing #regulation. The increase in price is commensurate to 12-30% of the increase in regulatory capital costs due to catastrophes, and the increase in price is larger for areas with higher hurricane risks, suggesting that consumers in risky areas bear the cost of #climatechange.

Do Finance Researchers Address Sample Size Issues? – A Bayesian Inquiry in the AI Era.

Traditional #statistical and #algorithm-based methods used to analyze #bigdata often overlook small but significant evidence. #bayesian #statistics, driven by #conditional #probability, offer a solution to this challenge. The review identifies two main applications of Bayesian statistics in #finance: prediction in financial markets and credit risk models. The findings aim to provide valuable insights for researchers aiming to incorporate Bayesian methods and address the sample size issue effectively in #financial #research.

How to Evaluate the Risks of Artificial Intelligence: A Proportionality‑Based, Risk Model

Date : Tags : , , , , , , ,
"The #eu proposal for the #artificialintelligenceact (#aia) defines four #risk categories: unacceptable, high, limited, and minimal. However, as these categories statically depend on broad fields of application of #ai systems (#ais), the risk magnitude may be wrongly estimated, and the AIA may not be enforced effectively. Our suggestion is to apply the four categories to the risk #scenarios of each AIs, rather than solely to its field of application."

Distributed Insurance: Tokenization of Risk and Reward Allocation

This paper claims to contribute to the understanding of #peertopeer, #decentralized distributed #insurance as a viable alternative to traditional insurance models, offering potential solutions to address market consolidation and enhance #financialinclusion through #risksharing. Further exploration and empirical studies are necessary to validate the viability and long-term implications of this emerging paradigm in the #insuranceindustry.

Quantum Computing: A Bubble Ready to Burst or a Looming Breakthrough?

This paper explores three notable #quantumcomputing applications in the realm of #centralbanking, including assessing #financialrisk, #creditscoring, and #transactionsettlement. Although these applications are currently in the proof-of-concept stage, they highlight the emergence of new software paradigms and potential breakthroughs. It also emphasizes the importance of carefully considering the trade-off between adopting innovative technology before it becomes mainstream and the #risk of being outpaced by more agile competitors.

CEO Risk‑Culture, Bank Stability and the Case of the Silicon Valley Bank

"We use the recently failed #svb as a case study. Our [#machinelearning #textanalysis] findings indicate a weaker emphasis on #riskgovernance by SVB and an environment, particularly after 2011, where the #ceo became more dominant in influencing SVB’s #riskculture. We also show that despite recognition of the portfolio problems, SVB’s CEO’s tone indicated that #regulatorycompliance and #riskstrategy of the #bank would #mitigate these #risks. We observe an alignment between the #riskculture of SVB and other banks with the highest uninsured deposits as well as with two #us #gsibs."

A Rumsfeldian Framework for Understanding How to Employ Generative AI Models for Financial Analysis

Date : Tags : , , , , , ,
This paper explores the use of #generativeai models in financial analysis within the Rumsfeldian framework of "known knowns, known unknowns, and unknown unknowns." It discusses the advantages of using #ai #models, such as their ability to identify complex patterns and automate processes, but also addresses the #uncertainties associated with generative AI, including #accuracy concerns and #ethical considerations.

Using Differential Privacy to Define Personal, Anonymous and Pseudonymous Data

This paper introduces the concept of differential privacy (DP) as a novel technical tool that can quantifiably assess the identification #risks of #databases, thereby aiding in the classification of data. By allocating a privacy budget in advance, data controllers can establish auditable and reviewable boundaries between #personal, #anonymous, and #pseudonymous data, while integrating this framework into broader data #riskmanagement practices.