18 résultats
pour « banking »
The banking industry faces complex financial risks, including credit, market, and operational risks, requiring a clear understanding of the aggregate cost of risk. Advanced AI models complicate transparency, increasing the need for explainable AI (XAI). Understanding risk mathematics enhances predictability, financial management, and regulatory compliance in an evolving landscape.
This study analyzes the financial impact of Corporate Social Irresponsibility (CSI) events on European banks using a dataset of 11,832 reputational shocks from 2007-2023. Results show significant negative stock returns and increased volatility following CSI media coverage, with proactive ESG engagement mitigating these effects.
Open banking creates diverse models: competitive and monopolistic banks. Policy changes impacting relative profitability lead banks to shift types. Increased capital requirements favor competitive banks, potentially raising system risk. Deposit rate ceilings can increase risk by promoting growth in the riskier competitive sector. Introducing a shadow banking sector benefits monopolistic banks, reducing overall system risk.
"Since the global financial crisis of 2007–9, legal risk has become increasingly important for the banking sector. In Poland, the growth in importance is predominantly associated with the so-called regulatory tsunami, which has seen a constantly changing legal framework for bank operations..."
The essential role of #ai in #banking holds promise for efficiency, but faces challenges like the opaque "black box" issue, hindering #fairness and #transparency in #decisionmaking #algorithms. Substituting AI with Explainable AI (#xai) can mitigate this problem, ensuring #accountability and #ethical standards. Research on XAI in finance is extensive but often limited to specific cases like #frauddetection and credit #riskassessment.
In March 2023, rapid #bankruns led to the failures of #siliconvalleybank, Signature Bank, and First Republic Bank. Uninsured depositors lost confidence due to higher interest rates and their investment model. Other banks are also experiencing deposit outflows. A book by #nyustern faculty and others analyzes the situation, offering a diagnosis and policy proposals for #financialresilience, emphasizing adaptable and robust #banking policies amidst changing #risks.
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.
This paper discusses the role of #centralbanks in #regulating and #supervising #esgrisks in the #banking sector. The authors review recent international and regional rules requiring banks to consider #esg factors in their #governance, and analyze the practices of #microprudential #supervisors in several jurisdictions.
We define the degree of #banking integration in the #eurozone through different phases of the #economic cycle, from 2006 to 2020, with #complexnetworks and #clusteralgorithms … Regarding the nodes of the network, #germany yields the position of centrality in favor of #france.
Monograph on accounting disclosure by banking institutions explores banking specificities, presents workhorse models, and illustrates specific applications of the models to inform policy.