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This academic paper proposes these 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀:

• The analysis provides a framework for introducing index insurance in competition with traditional products, emphasizing demand and solvency.

• Key drivers for index insurance demand are policyholder risk aversion, compensation speed advantage over traditional products, and its pricing (loading factor).

• The proposed hybrid product effectively balances the strengths of both insurance types by applying index insurance where it is “most suitable for policyholders,” accelerating compensation, and potentially reducing premiums.

• The methodology can help insurers identify specific loss types for which index compensation is preferred, optimizing portfolio structure and claims management.

• Future work will address modeling demand for index insurance in situations where traditional indemnity-based insurance is unavailable, requiring a “more nuanced approach to calibrate the utility function.”

This document introduces a novel two-step methodology for money laundering detection that significantly improves upon existing rule-based and traditional machine learning methods. The first step involves representation learning using a transformer neural network, which analyzes complex financial time series data without requiring labels through contrastive learning. This self-supervised pre-training helps the model understand the inherent patterns in transactions. The second step then leverages these learned representations within a two-threshold classification procedure, calibrated by the Benjamini-Hochberg (BH) procedure, to control the false positive rate while accurately identifying both fraudulent and non-fraudulent accounts, addressing the significant class imbalance in money laundering datasets. Experimental results on real-world, anonymized financial data demonstrate that this transformer-based approach outperforms other models in detecting fraudulent activities.

This consultation package is aimed at easing the reporting burden on insurance and reinsurance companies under the Solvency II framework. The proposed amendments seek to reduce reporting requirements by at least 26% for solo undertakings and 36% for small and non-complex undertakings. Key changes include reducing template frequency, deleting annual templates, and introducing technical simplifications. The EIOPA expects these changes to substantially reduce the burden on European insurers without compromising policyholder protection or financial stability. Stakeholders can provide feedback via the EU Survey until October 10, 2025.

This study develops a machine learning framework to identify high-risk enterprise financial reports, comparing Support Vector Machine, Random Forest, and K-Nearest Neighbors models. Using 2020–2025 audit data from the Big Four firms, Random Forest showed the highest performance (F1-score: 0.9012), excelling in detecting fraud and compliance issues. While KNN struggled with high-dimensional data, SVM performed well but was computationally intensive. The study highlights the potential of machine learning in auditing but notes limitations, including reliance on structured data and exclusion of external economic factors.

En 2024, Tracfin a franchi le cap des 200 000 déclarations de soupçon, avec 211 165 signalements (+13,2 % par rapport à 2023), reflétant l’engagement croissant des 50 professions assujetties à la lutte contre le blanchiment de capitaux (LCB-FT). Le secteur financier domine (93,1 %), mais le non-financier progresse (+25,7 %), notamment les opérateurs d’art (+254,4 %). Deux nouvelles professions, les entreprises de jeux numériques et gestionnaires de crédit, intègrent le dispositif. Tracfin renforce la qualité des déclarations via des échanges avec les déclarants et consolide sa coopération internationale, notamment avec l’AMLA et le Groupe Egmont.

This report examines how European (re)insurers address biodiversity risks, which threaten financial stability due to their complexity and links with climate risks. Despite challenges in quantifying impacts, one in five insurers references biodiversity in their risk assessments, though mostly qualitatively. Promising practices show growing awareness, but regional variations and limited metrics hinder progress. EIOPA calls for enhanced collaboration to improve data, models, and risk management, emphasizing the need to better understand the climate-biodiversity nexus and explore nature-based solutions to address insurance gaps.

Cette cartographie annuelle des risques, souligne la résilience des marchés financiers malgré un contexte mondial incertain marqué par des tensions géopolitiques et commerciales. L'AMF constate une volatilité accrue sur toutes les classes d'actifs qui devrait persister. Les prévisions de croissance mondiale ont été revues à la baisse. Bien que les marchés aient fait preuve de résilience face aux ajustements récents, des risques de correction futurs subsistent. La gestion d'actifs française a bien résisté, mais l'AMF reste vigilante sur les fonds immobiliers commerciaux et les actifs illiquides. Les cyberattaques sont en hausse, et l'entrée en vigueur du règlement DORA vise à renforcer la résilience opérationnelle.

Le bilan agrégé des six principales banques françaises a progressé de 3,4% en 2024, atteignant 8 801,5 milliards d'euros, principalement grâce à une hausse des titres de dette détenus (+17,1%) et des prêts aux institutions financières. Le Produit Net Bancaire (PNB) a atteint un niveau historique de 158,7 milliards d'euros (+8%), porté par les commissions et les activités de marché, malgré un léger recul de la Marge Nette d'Intérêts. La rentabilité s'améliore, avec une hausse du résultat net de 11,7%. La situation de solvabilité (ratio CET1 à 15,6%) et de liquidité reste solide, bien que le risque de crédit ait légèrement augmenté dans un contexte macroéconomique incertain.

L'activité d'assurance-vie en France a connu une forte croissance en 2024, avec une collecte brute record de 141,8 milliards d'euros, tirée par les supports en euros. Les rachats ont diminué de 10% par rapport à 2023. La collecte nette est redevenue positive, atteignant 22,8 milliards d'euros, malgré une collecte nette négative pour les supports en euros. Dans le même temps, l'assurance non-vie a vu ses primes augmenter plus rapidement que les sinistres, améliorant le ratio combiné à 96,9%. Le ratio de solvabilité moyen des assureurs a légèrement baissé à 238% fin 2024, dû à une diminution des fonds propres et une hausse du capital de solvabilité requis.

This study examines how European insurance companies influence mutual fund stability, particularly during periods of significant net outflows. Utilizing Solvency II and Lipper/Eikon data, the study reveals that insurers exhibit contrarian trading behavior, purchasing fund shares when other investors divest, especially in fixed-income funds. This behavior is more pronounced for affiliated funds. The paper also finds that insurers' financial health, indicated by solvency ratios, impacts their ability to act as contrarian traders; lower solvency ratios correlate with fewer purchases during outflows. Funds with insurer investments demonstrate enhanced resilience, exhibiting lower flow-to-performance sensitivity and reduced flow volatility. The findings suggest insurers can mitigate investor runs, but their stabilizing influence may lessen under systemic stress affecting their own financial health.