Extending and Improving Current Frameworks for Risk Management and Decision‑Making

The paper discusses the importance of #riskinformed #decisionmaking -and the use of #riskassessment to support decisions. It highlights the need for a more dynamic approach to #riskmanagement, which takes into account #uncertainty, changes in systems, phenomena, or values that could alter the underlying premises of the initial risk assessment.

Risk Sharing in Blockchain‑Based Insurance with Costs

This study examines the #riskallocation problem in distributed #insurance using #blockchaintechnology, considering different charging methods. Through #gametheory analysis, the research explores the #pareto optimal risk allocation method. The findings reveal that when charges occur during insurance signing, risk is proportionally distributed based on policyholders' #riskaversion coefficient. However, if the platform provider charges a fee proportional to the premium or actual risk, policyholders bear increased risk from others while their own risk is reduced, leading to decreased overall utility. These conclusions provide valuable insights for #blockchain insurance companies regarding user #riskmanagement and allocation.

The Unconscious Conscience of Digital Transformation: The Chief Compliance Officer

This paper explores the evolving role of #compliance in #digitaltransformation (#dt), as corporations globally embrace technology to enhance competitiveness and address responsible, #ethical, and #sustainable practices. It analyzes the current and potential role of Compliance in DT, emphasizing the need to manage #governance, #risk, and compliance aspects and leverage #esg objectives. The authors conducted interviews with Compliance heads and facilitated a Salon attended by General Counsel and Compliance professionals. The purpose is to encourage international discussions on Compliance's role in digital transformation.

The information value of past losses in operational risk

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"We show that past #operationalrisk losses are informative of future losses, even after controlling for a wide range of financial characteristics. We propose that the information provided by past losses results from their capturing hard-to-quantify factors such as the quality of operational risk controls, the #riskculture and the #riskappetite of the #bank."

Application of Deep Reinforcement Learning in Asset Liability Management

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This paper introduces the application of Deep Reinforcement Learning (#drl) in #alm, addressing limitations of traditional methods reliant on human judgement. The findings highlight the potential of DRL to enhance #riskmanagement outcomes for #insurers, #banks, #pensionfunds, and #assetmanagers, providing improved adaptability to changing market conditions.