This article deals with the issue of conflicts of interest in the selection of collective investment schemes in Switzerland. Asset management and investment advice have changed considerably over the past four decades. While the first guidelines after the Credit Suisse scandal in 1977 outlined simple forms of investment such as fiduciary and fixed-interest investments, the focus today is on access to alternative investments such as hedge funds and private equity. Economic relationships result in conflicts of interest. This article examines the legal framework and focuses on analysing the significance of conflicts of interest in the financial sector, particularly in connection with the duty of loyalty towards clients. It explains how the Financial Services Act (FinSA) contributes to the identification and management of such conflicts of interest.

Aperçu de la jurisprudence de l’année 2024 avec résumés en français ou en allemand des 39 arrêts les plus significatifs.

This contribution evaluates the effectiveness of AT1 bonds in fulfilling their intended functions, with a particular focus on their role in mitigating systemic risk and absorbing losses on a going-concern basis. Using the Credit Suisse case during the March 2023 banking crisis as a reference, we highlight how the CHF 16 billion write-down of AT1 bonds exposed critical weaknesses, including their delayed loss-absorbing function and unintended perverse incentives. While we do not yet advocate for the abolition of AT1 bonds, we explore potential reforms to enhance their effectiveness. These measures include introducing an automatic early trigger for suspending coupon payments or bond redemptions, raising quantitative thresholds, and integrating market-based indicators. Additionally, legal and tax considerations – such as the absolute priority rule, withholding tax exemptions, interest payments for income tax purposes – are addressed. If enhancing the loss-absorbing function of AT1 bonds on a going-concern basis proves unfeasible without introducing excessive complexity, banks may ultimately have no choice but to accept a simpler and more effective alternative : raising common equity tier 1 capital requirements.

La 2e édition de cet ouvrage livre une analyse approfondie et précise des notions fondamentales du DPE en vigueur au 30 novembre 2024 (compétence du juge suisse, application territoriale du DPE, responsabilité de l’entreprise et de ses personnes physiques, etc.). Le précis traite en outre d’une sélection d’infractions au premier plan de la lutte contre la criminalité économique et financière (blanchiment d’argent, défaut de vigilance en matière d’opérations financières, crime organisé, corruption, etc.). S’y ajoutent l’analyse du volet de prévention du blanchiment d’argent, y compris ses réformes successives ainsi que la protection des whistleblowers.

L’ouvrage intègre les récentes évolutions législatives (not. la révision en cours du régime de lutte contre le blanchiment d’argent et du droit de l’UE en la matière, le projet LTPM et les derniers développements en matière de lutte contre le terrorisme et le crime organisé).

In the financial sector, artificial intelligence (AI) is a major asset capable of transforming the services provided to clients, particularly by improving investment advice. Conversational agents have been deployed by some fintech companies and presented as investment assistants. However, the responses generated by these AI systems raise the question of their classification under financial regulation. Are they financial analyses ? Or are they rather personalized recommendations or advertising ? This contribution explores these different classifications, proposes criteria to delineate these concepts, and offers a classification based on the analysis of results generated by a sample of conversational agents, while also examining the potential regulatory implications.

The banking and finance industry was ahead of the curve in reducing human intervention in its operations. As early as the 1970s, communication technologies prepared for the rise of algorithmic trading. Back to the future, machine learning systems differ from rule-based (or symbolic) systems where logic dominates. They rely on data, analyse trends and inferences to generate plausible, but not exact, results. From a legal perspective, the shift from deterministic systems to systems making decisions on a fully or partially autonomous basis raises new interpretative questions. Since our legal system continues to hold users accountable for machine acts or omissions, this contribution emphasizes the leverage effect triggered by the combination of digitization and automation in the case of a mistake. To support our line of reasoning, we will identify three scenarios : contracts concluded between individuals in person, contracts facilitated through a simple digital intermediary, and contracts entered into entirely without human intervention.