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
This article reviews the EU's Artificial Intelligence Act, highlighting its structure, scope, and key principles like fairness and transparency. It critiques the complexity of regulating high-risk AI, forbidden practices, and the risk of hindering responsible innovation despite an overall balanced framework.
“... the paper analyses (i) how the AI Act should be applied and implemented according to its original intention of a risk-based approach, (ii) how the AI Act should be complemented by sector-specific legislation in the future to avoid inconsistencies and over-regulation, and (iii) what lessons legislators around the world can learn from the AI Act in regulating AI.”
“This study explores the impact of AI on auditing through a Systematic Literature Review to develop a Conceptual Framework for auditing practices.”
“… the present study developed the AI Categorization and Classification (AI-CC) Method as a central artifact to provide guidance on the use of AI within the profession. The target users of the AI-CC Method are regulators, standard setters, the strategic management of the Big Four, and individual auditors.”
"The essay presents a vision of the AI-enhanced actuary, who leverages AI to build more accurate and efficient models, incorporates new data sources, and automates routine tasks while adhering to professional and ethical standards. We also discuss the challenges and speed-bumps along the way, including explainability, bias and discrimination risks, regulatory hurdles, and the need for actuaries to acquire AI knowledge and skills."
This paper outlines the need for AI risk regulation due to documented harms caused by AI systems. It cites examples of proposed and enacted laws aimed at mitigating these risks but highlights challenges in quantifying harms. It criticizes a bias towards technocorrectionism and advocates for a broader regulatory approach to address AI's impacts effectively.