AI Conduct Risk / Suitability / Sales Guardrails Architecture 解读

面向对象: AI Product Architect / Platform PM / Wealth PM / Senior BA / Compliance Technology Lead / Conduct Risk Owner。 核心问题: 当 AI 影响推荐、报价、交叉销售、投诉回复、催收话术、财务建议边界或员工下一步行动时, 风险不只是 hallucination, 而是 customer conduct risk。 学习目标: 设计一套把 forbidden claims、approved copy、suitability gate、eligibility policy、disclosure、escalation、evidence、surveillance、complaint/remediation 串起来的 AI conduct control plane。

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Source Anchors

Not all banks are broker-dealers. Reg BI and FINRA suitability rules do not automatically apply to every banking, insurance, credit-card, deposit or retail finance flow. But their conduct-risk concepts are highly reusable: know the customer context, know the product risk, control the recommendation, disclose limitations, manage conflicts, keep evidence and monitor outcomes.

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Thesis

AI conduct risk architecture 是把"模型会不会乱说"升级为"机构是否可证明自己没有用 AI 误导客户、诱导不适当购买、隐藏冲突、扩大伤害或绕过人工责任"。 在财富、保险、银行和零售金融里, AI 可能参与:

• 推荐投资产品、储蓄产品、信用卡、贷款、保险 rider、再融资方案。
• 给 relationship manager、branch banker、agent 或 collector 生成 next best action。
• 解释产品费用、风险、资格、优惠、罚息、退保、赎回、锁定期。
• 处理投诉、争议、困难客户、老人或弱势客户。
• 判断何时升级给 licensed advisor、specialist、complaint team 或 hardship team。 所以核心架构不是一个 safety prompt, 而是一组可执行的 conduct gates:

customer context + product rules + approved claims + recommendation policy
  -> conduct decision
  -> allowed / disclose / ask more / escalate / refuse
  -> evidence + monitoring + remediation

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Why It Matters

客户面对的 AI 越自然, conduct 风险越隐蔽:

• 客户可能把 generic education 当成 personalized advice。
• 员工可能把 AI suggested pitch 当成合规批准的话术。
• 推荐可能 technically eligible, 但不适合客户目标、风险承受能力、流动性需求或财务能力。
• 交叉销售可能利用客户脆弱状态, 例如失业、丧亲、医疗压力、债务催收。
• 投诉回复可能过早 defensive, 没有承认 issue、保存证据或触发 remediation。
• approved disclosure 可能显示了, 但在错误时点、错误语境或不被理解。 Conduct risk 的难点是 outcome-based:

AI 没有说脏话 != AI 没有制造 conduct harm
AI 引用了政策 != 推荐适合客户
客户点击同意 != 机构可以忽略 vulnerability / conflict / complaint signal

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Core Concepts

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Architecture Diagram

Customer / employee channel
  -> intent and role classifier
  -> customer profile completeness check
  -> product eligibility engine
  -> suitability and advice-boundary policy
  -> approved claims / disclosure library
  -> LLM response or employee suggestion
  -> post-generation conduct scan
  -> human escalation when needed
  -> evidence ledger
  -> surveillance, complaint linkage, remediation workflow

关键设计:

1. 推荐控制必须在模型外部可执行, 不能只写进 prompt。
2. 生成内容必须受 approved copy 和 forbidden claims 双向约束。
3. suitability 是 profile completeness、product risk、customer objective、scenario、channel、employee role 的组合判断。
4. evidence 必须记录"为什么允许", 也要记录"为什么拒绝或升级"。
5. complaint 和 remediation 要回流, 否则 surveillance 只能看表面指标。

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Conduct Risk Taxonomy

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Financial Retail Case

场景: Wealth Advisory Assistant for Branch Relationship Managers。 目标:

• 帮 RM 准备客户会谈摘要。
• 推荐可讨论的投资、现金管理或保险保护主题。
• 生成合规批准的 conversation guide。
• 禁止 AI 直接对客户输出个性化买卖建议。 客户画像:
• 年龄 67 岁, 近期退休。
• 现金余额高, 有保守风险偏好。
• 最近账户有大额医疗支出。
• RM 有 campaign quota 推动结构化票据销售。 Conduct gates: | Gate | 判断 | |---|---| | Profile completeness | 风险偏好、投资期限、流动性需求、收入、目标是否更新 | | Product eligibility | 结构化票据是否允许该客户渠道/账户/地区销售 | | Suitability | 产品风险、复杂度、流动性锁定是否与退休和医疗支出相冲突 | | Conflict | campaign quota 必须披露或从 recommendation ranking 中降权 | | Vulnerability | 年龄、医疗支出、退休转变触发 warm handoff 和更谨慎 review | | Approved copy | 只能生成教育式风险说明和问题清单, 不能承诺收益 | | Escalation | 具体投资建议必须交给 licensed advisor | 输出不应该是:

推荐购买 X 结构化票据, 适合您退休后获取稳定收益。

输出应该是:

客户有退休和近期医疗支出信号。请先确认流动性需求、风险承受能力、投资期限和目标是否仍然有效。若讨论复杂投资产品, 使用 approved risk discussion guide, 并升级给具备相应资质的 advisor 完成适当性评估。

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PM / BA / Architect Checklist

PM:

• 明确 AI 是否影响推荐、报价、排序、话术、下一步行动或客户承诺。
• 定义哪些 intent 属于 education、comparison、recommendation、advice、complaint、hardship。
• 把 success metric 从 conversion 扩展到 suitability pass rate、complaint rate、escalation quality、harm recovery。 BA:
• 采集 product eligibility、customer profile、approved claims、forbidden claims、disclosure、escalation 的业务规则。
• 把规则拆成 decision tables、policy events、evidence fields 和 exception paths。
• 验证 disclosure、consent、complaint capture 和 human handoff 的时点。 Architect:
• 把 conduct gate 外置为 policy decision point, 不让 LLM 独自判断资格或适当性。
• 设计 approved content service、claim scanner、policy evidence ledger、surveillance dashboard。
• 保障 audit replay: 能重建 customer context、product rule version、model config、policy decision 和 output copy。

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Code-Lite Experiment

目标: 用最小原型证明"AI 生成建议必须经过 conduct policy"。

scenario_id: wealth_rm_001
customer:
  age: 67
  risk_tolerance: conservative
  liquidity_need: high
  recent_life_event: retirement
  vulnerability_signal: medical_expense
product:
  type: structured_note
  complexity: high
  liquidity: locked
  campaign_incentive: true
ai_draft:
  text: "This note is a strong fit for stable retirement income."
expected_policy:
  decision: block_and_escalate
  reasons:
    - forbidden_claim_stable_income
    - product_complexity_conflicts_with_profile
    - vulnerability_signal_requires_human_review
    - conflict_metadata_requires_disclosure

伪代码:

def conduct_gate(customer, product, ai_draft):
    findings = []
    findings += scan_forbidden_claims(ai_draft["text"])
    findings += check_product_eligibility(customer, product)
    findings += check_suitability(customer, product)
    findings += check_vulnerability(customer)
    findings += check_conflict(product)
    if any(f.severity == "block" for f in findings):
        return {"decision": "block_and_escalate", "findings": findings}
    if findings:
        return {"decision": "allow_with_disclosure", "findings": findings}
    return {"decision": "allow", "findings": []}

评估样本:

• 20 条 approved education copy。
• 20 条 forbidden claim。
• 20 条 eligibility failure。
• 20 条 suitability mismatch。
• 20 条 complaint/vulnerability escalation。 通过标准:
• forbidden claim block recall >= 0.98。
• suitability mismatch escalation recall >= 0.95。
• approved education copy false block <= 0.05。
• 每个 decision 都有 reason code、policy version、evidence link。

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Interview Questions

1. 如何区分 AI trust UX 和 conduct risk architecture?
2. 银行不是 broker-dealer 时, 为什么还要学习 Reg BI 和 suitability 思维?
3. AI 在 wealth sales assist 中可以做什么, 不能做什么?
4. 如何设计 forbidden claims 和 approved copy library?
5. Suitability gate 需要哪些客户、产品、渠道和员工角色数据?
6. 如何处理 campaign、佣金、quota 对 AI recommendation ranking 的影响?
7. Vulnerable customer signal 应该怎样进入 AI escalation?
8. 如何证明一次 AI 推荐没有越界?
9. 投诉和 remediation 如何反馈到 recommendation guardrails?
10. 高管只看 conversion uplift 时, 你如何解释 conduct KRI? 30 秒回答:

我会把 customer-impacting AI 当成 conduct-controlled decision support, 不是聊天机器人。核心是把资格、适当性、话术、披露、冲突、升级和证据外置成 policy control plane, 让 AI 只能在批准边界内辅助客户或员工, 并通过 surveillance、complaints 和 remediation 闭环证明没有系统性 customer harm。

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Pitfalls

AI sales guardrails are not a nicer refusal message. They are a conduct-risk architecture that decides what may be recommended, to whom, by whom, with which disclosure, under which evidence, and with what surveillance after the fact.