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Bank T166 Retail Finance Card Churn Warning Assistant

ASecurity

当用户需要在银行零售金融场景下,对信用卡客户的流失风险进行持续监测、信号识别、分层预警与行动建议时使用本技能。适合输出预警名单、风险级别、触达优先级与挽留动作建议。

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  • Added June 6, 2026
documentationpythonbash

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npx -y skills add aifinlab/FinClaw --skill bank-t166-retail-finance-card-churn-warning-assistant --agent claude-code

Installs into .claude/skills of the current project.

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SKILL.md
---
name: bank-t166-retail-finance-card-churn-warning-assistant
description: "当用户需要在银行零售金融场景下,对信用卡客户的流失风险进行持续监测、信号识别、分层预警与行动建议时使用本技能。适合输出预警名单、风险级别、触达优先级与挽留动作建议。"
---

# 信用卡流失预警助手

本技能用于把信用卡客户的“流失信号”转化成可执行的预警名单与挽留动作包。强调客群分层、信号解释、行动优先级和合规触达边界,不替代审批或收益承诺。

## 适用范围
- 信用卡客户流失预警与经营挽留
- 运营侧周报/月报、活动触达名单生成
- 客户经理优先级清单与回访建议
- 服务风险复盘与留存专项跟踪

## 何时使用
- 需要识别信用卡消费、活跃度、账单行为的异常趋势
- 需要把流失信号转为“可执行名单+动作建议”
- 需要统一预警口径、批次管理和复盘节奏

## 何时不要使用
- 用户要求直接下结论(停卡/降额/处罚)或做违规承诺时
- 没有时间窗口、样本清单或监测口径时

## 默认工作流
1. 明确批次、时间窗口与预警口径(消费/活跃/账单)
2. 识别高风险信号并排序,生成优先处理名单
3. 区分“可挽留”与“已流失/需核验”的客户
4. 输出动作建议、补充信息清单与复盘指标

## 重点分析框架
- 消费与活跃下降是否持续、是否超出历史基线
- 账单行为是否出现延迟、异常或不良趋势
- 客群价值与成本匹配(高价值优先挽留)
- 服务投诉或体验问题是否是直接触发因素
- 合规授权、触达频控与禁用话术是否满足

## 输入要求
- 最低可用输入:`batch_id`、`time_window`、`customers`
- 推荐输入字段见 `references/input-schema.md`

## 输出要求
- 风险分层名单(高/中/低)与排序理由
- 关键流失信号与解释
- 可执行挽留动作建议
- 风险提示、信息缺口与下一步建议

## 风险与边界
- 不得将监测信号直接写成违规或违约事实
- 不得输出收益承诺、违规营销或强制性结论
- 触达必须遵守授权与频控规则

## 信息不足时的处理
- 先输出已确认信息与缺口,再给出最低可行动建议
- 缺失授权、联系方式或历史基线时,必须显式标注

## 交付标准
- 至少回答“现状如何、为何判断、下一步做什么”
- 输出可被运营或客户经理直接执行

## 配套脚本

```bash
python scripts/run_skill.py --input assets/example-input.json --format markdown
python scripts/run_skill.py --input assets/example-input.json --format json
```

脚本入口:`scripts/run_skill.py`(调用 `shared/retail_service_ops_skill_engine.py` 的 `t166` 场景)。

Files in this skill

  • SKILL.md2.8 KB
  • assets/example-input.json1.1 KB
  • assets/templates/card-churn-warning-report.md358 B
  • assets/templates/escalation-note.md142 B
  • assets/templates/retention-action-plan.md187 B
  • references/churn-signal-playbook.md460 B
  • references/input-schema.md1.3 KB
  • references/output-schema.md1 KB
  • references/retention-action-guide.md372 B
  • references/risk-escalation-criteria.md314 B
  • scripts/run_skill.py1.3 KB

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