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This article explores AI's societal risks, including harm to communities, security threats, and existential risks. It proposes a framework for systemic AI regulation, advocating a precautionary approach focusing on technology rather than specific applications. The article suggests principles for cohesive regulation, including oversight and diverse regulatory strategies.
The book divides into four parts. Part I introduces machine learning in finance, tracing its history. Part II covers practical aspects like model implementation, laden with formulas. Part III details supervised, unsupervised, and reinforcement learning in asset management with case studies. Part IV tackles ethics, regulations, risk, and future trends, aiming for a holistic understanding.
In 1921, Keynes and Knight stressed the distinction between uncertainty and risk. While risk involves calculable probabilities, uncertainty lacks a scientific basis for probabilities. Knightian uncertainty exists when outcomes can't be assigned probabilities. This poses challenges in decision-making and regulation, especially in scenarios like AI, urging caution for eliminating worst-case scenarios due to potential high costs and missed benefits.
The paper addresses challenges in risk assessment from limited, non-stationary historical data and heavy-tailed distributions. It introduces a novel method for scaling risk estimators, ensuring robustness and conservative risk assessment. This approach extends time scaling beyond conventional methods, facilitates risk transfers, and enables unbiased estimation in small sample settings. Demonstrated through value-at-risk and expected shortfall estimation examples, the method's effectiveness is supported by an empirical study showcasing its impact.
The paper explores convex risk measures with weak optimal transport penalties, demonstrating explicit representations via nonlinear transformations of loss functions. It delves into computational aspects, discussing approximations using neural networks and applies these concepts to diverse examples. Finally, it demonstrates practical applications in insurance and finance for worst-case losses and no-arbitrage pricing beyond quoted maturities.
Amid a surge in corporate social responsibility (CSR) communication, this study delves into the prevalence of symbolic CSR actions versus substantive efforts. Focusing on US-listed firms, it links CSR decoupling with heightened financial fraud risks. Factors like governance, audit quality, and ownership concentration amplify this vulnerability, emphasizing caution for stakeholders and regulators when assessing CSR claims.
This study explores cyber risk in businesses, suggesting cybersecurity investment and insurance as key strategies. Using a network model, it examines firms' interconnected decisions, defining a Nash equilibrium where firms optimize cybersecurity and insurance. Findings highlight their interdependence and how network structures affect choices, reinforced by numerical analyses.
The paper explores how advanced technologies like AI pose both potential and complexity in risk and safety applications. It delves into explainability and interpretability within risk science, emphasizing their role in enhancing assessment, management, and communication of risks, illustrated with autonomous vehicles examples. Aimed at stakeholders navigating tech's impact on risk.
The paper delves into the intertwining of financial institutions and environmental concerns, particularly climate change and biodiversity loss. It introduces a dual framework based on 'impact' and 'risk' to explore their complex relationship. It analyzes the co-existing but sometimes opposing approaches at their interface, elucidating how finance, climate change, and biodiversity intertwine in the realm of "sustainable finance".
The paper explores optimal insurance contracts using decision makers' preferences, combining expected loss with a deviation measure like Gini coefficient or standard deviation. It reveals that using expected value principle favors stop-loss indemnities, defining precise deductibles. The optimal indemnity structure remains consistent even with a capped insurance premium. Multiple examples based on Gini coefficient and standard deviation illustrate these findings.