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Addressing Adversarial Machine Learning (𝗔𝗠𝗟) in financial systems is like designing a bank vault: not only must the vault be robust enough to withstand sophisticated attacks (𝗔𝗠𝗟 𝗱𝗲𝗳𝗲𝗻𝘀𝗲𝘀), but regulators also require that the complex mechanisms inside are transparent and explainable to auditors (𝗲𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀). Meanwhile, the bank must ensure that the security measures don't slow down transactions (𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗱𝗲𝗴𝗿𝗮𝗱𝗮𝘁𝗶𝗼𝗻/𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝘆 𝘁𝗿𝗮𝗱𝗲-𝗼𝗳𝗳) and that its staff has the specialized knowledge to operate and repair the mechanism (𝘀𝗸𝗶𝗹𝗹𝘀 𝗴𝗮𝗽).

The messages on the upcoming 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗢𝗺𝗻𝗶𝗯𝘂𝘀 provide the insurance industry's perspective and concrete recommendations regarding new European Union digital legislation. 𝗜𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲 𝗘𝘂𝗿𝗼𝗽𝗲 argues that greater simplification and alignment are urgently needed in the existing patchwork of regulations—including the 𝗔𝗜 𝗔𝗰𝘁, 𝗗𝗢𝗥𝗔, and 𝗚𝗗𝗣𝗥—which currently create complex, overlapping, and sometimes inconsistent obligations for insurers. The document outlines guiding principles for policymakers, such as achieving real burden reduction and coordinating rules across different regulatory levels, to ensure the new Digital Omnibus package is fit for purpose. Specific areas addressed include the need to clarify how existing financial services legislation (like 𝗦𝗼𝗹𝘃𝗲𝗻𝗰𝘆 𝗜𝗜 and 𝗜𝗗𝗗) applies to AI use, streamlining cyber incident reporting under DORA, and clarifying the legal basis for using personal data for AI training purposes. Ultimately, the paper seeks a more coherent regulatory framework to enable digital innovation and support the sector's contribution to Europe's economic resilience.

Cet article, basé sur une analyse d'Insurance Europe, souligne la complexité et la redondance des réglementations numériques européennes (AI Act, DORA, RGPD, etc.). Ce "labyrinthe numérique" crée des frictions opérationnelles et freine l'innovation.
L'objectif est une simplification stratégique basée sur des principes de clarté, de réduction des charges administratives et de coordination entre autorités.
Des recommandations précises sont proposées pour alléger le fardeau réglementaire :
• IA : Exclure les méthodes statistiques traditionnelles de l'AI Act et clarifier le chevauchement avec Solvabilité II.
• Services Cloud : Prioriser DORA sur Solvabilité II pour les services TIC critiques et reconnaître les audits tiers existants.
• Cybersécurité : Exempter les entités DORA du Cyber Resilience Act et instaurer un modèle européen unique de rapport d'incidents.
• Données : Aligner les évaluations d'impact (DPIA/FRIA) et adopter une approche plus réaliste de l'anonymisation pour stimuler l'innovation.
En simplifiant intelligemment, l'UE permettra aux assureurs d'évoluer vers une gestion proactive des risques numériques.

AI is not just an incremental improvement but a "paradigm shift" in regulatory compliance. By automating KYC, AML, and transaction monitoring, financial institutions can achieve unprecedented levels of efficiency, accuracy, and risk management. However, this transformative potential comes with significant responsibilities regarding data governance, ethical considerations, and maintaining human oversight. Success in this evolving landscape will hinge on strategic AI implementation, continuous adaptation to regulatory changes, and strong collaboration across the industry and with regulatory bodies. The long-term goal is a more "secure and resilient financial ecosystem."

This paper 𝗲𝘅𝗮𝗺𝗶𝗻𝗲𝘀 𝘁𝗵𝗲 𝗲𝘀𝗰𝗮𝗹𝗮𝘁𝗶𝗻𝗴 𝘁𝗵𝗿𝗲𝗮𝘁 𝗼𝗳 𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝗳𝗿𝗮𝘂𝗱 𝗮𝗻𝗱 𝗰𝘆𝗯𝗲𝗿𝗰𝗿𝗶𝗺𝗲, highlighting how criminal organizations are rapidly adopting advanced AI, particularly generative AI, to execute sophisticated attacks. It details how these malicious uses lead to 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝗱 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗹𝗼𝘀𝘀𝗲𝘀, 𝗺𝗼𝗿𝗲 𝗶𝗻𝘁𝗿𝗶𝗰𝗮𝘁𝗲 𝗰𝗿𝗶𝗺𝗲 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀, 𝗮𝗻𝗱 𝗻𝗼𝘃𝗲𝗹 𝘀𝗰𝗮𝗺 𝘁𝘆𝗽𝗼𝗹𝗼𝗴𝗶𝗲𝘀, such as deepfakes and advanced phishing. The document also 𝗲𝘅𝗽𝗹𝗼𝗿𝗲𝘀 𝘁𝗵𝗲 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝗳𝗮𝗰𝗲𝗱 𝗯𝘆 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗶𝗻𝘀𝘁𝗶𝘁𝘂𝘁𝗶𝗼𝗻𝘀 𝗶𝗻 𝗱𝗲𝗳𝗲𝗻𝗱𝗶𝗻𝗴 𝗮𝗴𝗮𝗶𝗻𝘀𝘁 𝘁𝗵𝗲𝘀𝗲 𝘁𝗵𝗿𝗲𝗮𝘁𝘀, citing issues like slow AI adoption, outdated risk management frameworks, and underinvestment in defense systems. Ultimately, it 𝗮𝗱𝘃𝗼𝗰𝗮𝘁𝗲𝘀 𝗳𝗼𝗿 𝘁𝗵𝗲 𝘂𝗿𝗴𝗲𝗻𝘁 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗼𝗳 𝗮𝗴𝗶𝗹𝗲, 𝗔𝗜-𝘃𝗲𝗿𝘀𝘂𝘀-𝗔𝗜 𝗱𝗲𝗳𝗲𝗻𝘀𝗲 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 and emphasizes the critical need for industry-wide cooperation to counteract the evolving landscape of AI-enabled financial crime.

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.

The European Commission’s AI Continent Action Plan emphasizes the need to significantly expand cloud and data center capacity across the EU to support AI and digital infrastructure goals. The Cloud and AI Development Act aims to incentivize investment and triple current capacity within seven years. The insurance sector supports this approach but warns against restrictive sovereignty measures that could exclude non-EU providers without viable alternatives. It advocates for flexible, risk-based cloud definitions and support for hybrid strategies. The sector stresses that capacity-building, not protectionism, is key to achieving digital sovereignty while maintaining innovation, competitiveness, and international interoperability.

AI could revolutionize UK sectors, enhancing productivity and decision-making, notably in finance by automating processes and refining decisions like underwriting. However, its rapid evolution raises uncertainties and financial stability risks, including systemic issues from flawed AI models, market instability, and cyber threats. The Financial Policy Committee (FPC) is assessing these risks to ensure safe AI adoption, supporting sustainable growth through vigilant monitoring and regulation.

The banking industry faces complex financial risks, including credit, market, and operational risks, requiring a clear understanding of the aggregate cost of risk. Advanced AI models complicate transparency, increasing the need for explainable AI (XAI). Understanding risk mathematics enhances predictability, financial management, and regulatory compliance in an evolving landscape.

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.