76 résultats pour « banks »
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The SRB updated its operational continuity in resolution (OCIR) guidance. It clarifies expectations for banks on service identification, risk assessment, and mitigation measures like resilient contracts and robust IT systems. The revisions align with recent frameworks like DORA and EBA guidelines. Minor additions will be applied from the 2026 resolution planning cycle, pending further regulatory developments.
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The paper examines climate litigation's growing impact on banks, noting limited current effects but a projected increase. Key risks include reputational damage and influences on risk management and investment decisions. Banks are urged to address climate litigation risks proactively to enhance resilience, with future research suggested on mitigation strategies.
Local communities exposed to #fraudulent #investmentadvisory firms tend to withdraw deposits from their affiliated #banks, even though the banks are not involved in the #misconduct. The #reputationalrisk is more significant when banks share names with fraudulent advisory firms or are located in areas with high social norms. The author establishes causality by exploring a quasi-natural experiment in which #fraud is likely exogenously revealed.
"#banks take costly actions (such as higher #capitalization, #liquidity holding, and advanced #riskmanagement) to avoid financial distress and #bankruns ... We show that #prudential #regulations have an informational impact: sufficiently tight regulations can eliminate inefficient separating equilibria in banks’ signaling game, thereby changing the information available to creditors and their incentives to run."
This paper examines the use of #machinelearning methods in the context of #banks' #capitalrequirements, specifically the internal Ratings Based (#irb) approach. The authors discuss the advantages and risks of using machine learning in this domain, and provide recommendations related to #risk parameter estimations, #regulatory capital, the trade-off between performance and interpretability, international #banking competition, and #governance, #operationalrisk, and training.
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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.