NEWS & ANALYSIS FROM BRAITHWATE’S EXPERTS.

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Consumer Duty Board reporting

A recent poll revealed that only 1% of firms have completed their annual Consumer Duty Board report. With the deadline of July 31, 2024, fast approaching, it's crucial for firms to expedite their efforts to avoid non-compliance and potential FCA sanctions.

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ESMA MiCA third consultation paper: a deep-dive into proposed market abuse requirements for crypto-assets

The proposed market abuse rules for crypto-assets under MiCA are based heavily on the existing TradFi rules set out in MAR. Potential challenges facing crypto firms operating in the EU include: the expansive scope of Persons Professionally Arranging or Executing Transactions (PPAETs); prohibition of certain business practices (e.g. MEV); and difficulty attributing responsibility for STOR submission.

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Singapore’s Generative AI Model Governance Framework: impacts on the Financial Services Industry

Singapore launched its first Model Governance Framework on traditional AI in 2019, later revising it in 2020. Updating the framework again is necessary amid emerging concerns and threat scenarios surrounding the use and development of generative AI. The main aim of the new Framework is to promote public understanding and trust in technologies enabling end-users to access generative AI confidently and safely.

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Announcing Braithwate’s AI/ML risk management service

If you are working on an AI-driven process or product and not sure how AI risks may impact your firm, please 👋 reach out for an initial consultation. At Braithwate and Holistic AI we have proven tools and methodologies to help you adopt AI with confidence.

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How to manage the risk of AI bias in Identity Verification

Braithwate and HAI share insights on AI bias risk posed by the adoption of automated Identity Verification solutions as part of increasingly digitised customer onboarding journeys. In order to mitigate AI bias risk and continue to deliver fair customer outcomes, we recommend firms review their technical and qualitative approaches to AI bias risk management.

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