Scholarlink

This section examines how regulation shapes the design, operation, and oversight of AI-enabled and automated investment services (“robo-advice”) across leading jurisdictions and distills the practical implications for firms building or scaling these models. We map convergence among functional regulators the U.S. Securities and Exchange Commission (SEC), the UK Financial Conduct Authority (FCA), the European Securities and Markets Authority (ESMA), and the Monetary Authority of Singapore (MAS)—showing that automation does not create a “lite” regime: fiduciary/suitability duties, fair marketing rules, technology and outsourcing controls, and record-keeping apply with equal or greater intensity when advice is delivered by algorithms. Horizontally, the EU’s Digital Operational Resilience Act (DORA) and AI Act tighten governance, transparency, and resilience expectations that directly touch model development, deployment, monitoring, and rollback. We translate these frameworks into operational control sets data capture and suitability mapping (including sustainability preferences in Europe), model governance and change control, performance and “AI” claim substantiation, digital engagement practice (DEP) inventories, third-party/cloud oversight, and audit-grade books and records illustrating each with real enforcement and supervisory findings. The section also explores how to balance innovation and investor protection using sandboxes, “targeted support”/simplified advice initiatives in the UK, outcomes testing under the Consumer Duty, and DORA-style resilience drills, so product teams can iterate safely. Case studies (Schwab cash floors; Wealthfront/Hedgeable feature and marketing claims; Titan’s hypothetical performance controls; Betterment’s TLH disclosures; SoFi conflicts; SEC “AI-washing”) show recurring failure modes and durable fixes: promise carefully, evidence continuously, explain plainly, and remediate fast. The overarching message is practical: build to the strictest common denominator across markets, wire compliance into the release pipeline, and treat every client-facing statement, especially numbers and AI claims, as a regulated representation supported by contemporaneous evidence.

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Regulatory and Compliance Issues in AI-Driven Financial Services

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  • 出版模式：

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  • 发表日期：

    2026年

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  • Narmin Nahidi

摘要

This section examines how regulation shapes the design, operation, and oversight of AI-enabled and automated investment services (“robo-advice”) across leading jurisdictions and distills the practical implications for firms building or scaling these models. We map convergence among functional regulators the U.S. Securities and Exchange Commission (SEC), the UK Financial Conduct Authority (FCA), the European Securities and Markets Authority (ESMA), and the Monetary Authority of Singapore (MAS)—showing that automation does not create a “lite” regime: fiduciary/suitability duties, fair marketing rules, technology and outsourcing controls, and record-keeping apply with equal or greater intensity when advice is delivered by algorithms. Horizontally, the EU’s Digital Operational Resilience Act (DORA) and AI Act tighten governance, transparency, and resilience expectations that directly touch model development, deployment, monitoring, and rollback. We translate these frameworks into operational control sets data capture and suitability mapping (including sustainability preferences in Europe), model governance and change control, performance and “AI” claim substantiation, digital engagement practice (DEP) inventories, third-party/cloud oversight, and audit-grade books and records illustrating each with real enforcement and supervisory findings. The section also explores how to balance innovation and investor protection using sandboxes, “targeted support”/simplified advice initiatives in the UK, outcomes testing under the Consumer Duty, and DORA-style resilience drills, so product teams can iterate safely. Case studies (Schwab cash floors; Wealthfront/Hedgeable feature and marketing claims; Titan’s hypothetical performance controls; Betterment’s TLH disclosures; SoFi conflicts; SEC “AI-washing”) show recurring failure modes and durable fixes: promise carefully, evidence continuously, explain plainly, and remediate fast. The overarching message is practical: build to the strictest common denominator across markets, wire compliance into the release pipeline, and treat every client-facing statement, especially numbers and AI claims, as a regulated representation supported by contemporaneous evidence.