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This research develops a taxonomy of operational risks impacting corporate sustainability. A literature review and analysis of 100 business cases reveal relationships between these risks, their causes, and their economic, social, and environmental consequences. The findings help companies classify and manage sustainability-related operational risks, though the specific relationships may vary across sectors and individual cases.
This lecture explores how probability theory can quantify uncertainty, chance, and even ignorance. He demonstrates methods to measure the quality of these quantified uncertainties. He also humorously admits a miscalculation during the lecture regarding paired comparisons within the audience.
Generative AI (GAI) is transforming banking risk management, improving fraud detection by 37%, credit risk accuracy by 28%, and regulatory compliance efficiency by 42%. GAI enhances stress testing but faces challenges in privacy, explainability, and skills gaps. Its adoption, led by larger banks, demands holistic strategies for equitable industry impact.
This study proposes a new method for detecting insider trading. The method combines principal component analysis (PCA) with random forest (RF) algorithms. The results show that this method is highly accurate, achieving 96.43% accuracy in classifying transactions as lawful or unlawful. The method also identifies important features, such as ownership and governance, that contribute to insider trading. This approach can help regulators identify and prevent insider trading more effectively.
Insurance typically benefits risk-averse individuals by pooling finite-mean risks. However, with infinite-mean distributions (e.g., Pareto, Fréchet), risk sharing can backfire, creating a "nondiversification trap." This applies to highly skewed distributions like Cauchy or catastrophic risks with infinite losses. Open questions remain about these complex scenarios.
Explainable AI (XAI) is becoming increasingly important, especially in fields like fraud detection. Differentiable Inductive Logic Programming (DILP) is an XAI method that can be used for this purpose. While DILP has scalability issues, data curation can make it more applicable. While it might not outperform traditional methods in terms of processing speed, it can provide comparable results. DILP's potential lies in its ability to learn recursive rules, which can be beneficial in certain use cases.
This study analyzes tone consistency in bank risk disclosures from regulatory Pillar 3 reports and annual IFRS reports. Findings indicate that optimistic P3 tones enhance annual report informativeness, while pessimistic tones can obscure it.
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.
Effective risk management requires understanding aggregate risks, individual business unit riskiness, and systemic risks. Realistic models must consider complex phenomena like heterogeneous marginals and excess kurtosis. A modified individual risk model using Multivariate Stable Distributions addresses these challenges, enabling tractable aggregation, dependence analysis, and Tail Conditional Expectation calculations for aggregate risks.
The paper explains Artificial Intelligence (AI), focusing on Generative AI, its role in finance, and its differences from Machine Learning. It covers AI’s applications in financial forecasting, risk management, and decision-making, while addressing benefits, challenges, regulations, and ethical concerns. It offers practical advice for adopting AI technologies in financial operations.Generative Artificial Intelligence for Finance Professionals