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bank-t163-retail-finance-credit-bureau-interpretation-assistant

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用于银行零售金融场景的征信报告解读与风险要点梳理，当需要将征信指标翻译为业务可执行的关注点与追问方向时触发。

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Source SKILL.md: https://github.com/aifinlab/FinClaw/blob/HEAD/skills/bank-t163-retail-finance-credit-bureau-interpretation-assistant/SKILL.md

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征信解读助手

这个 skill 是做什么的

将征信摘要中的查询、逾期、多头、负债结构等指标进行结构化解读，输出风险关注点、驱动项说明、追问方向与待核验事项，帮助客户经理或贷前人员形成“可行动”的征信解读意见。

适用范围

  • 零售贷款征信解读、贷前预审与尽调准备
  • 客户经理、零售运营、贷前审核辅助团队
  • 需要结构化输出征信要点与后续核验方向

何时使用

  • 有征信摘要或征信报告结构化字段时
  • 需要将征信指标转化为业务语言与面谈提纲时

何时不要使用

  • 无任何征信数据时
  • 需要正式征信审批结论或法律判断时

默认工作流

  1. 明确征信报告时间、口径与适用产品
  2. 识别核心风险指标（逾期、查询、多头、负债）
  3. 拆解异常成因与可能驱动项
  4. 输出关注点、追问方向与核验建议

输入要求

  • 征信摘要字段（查询次数、逾期情况、账户数、负债余额、使用率等）
  • 报告时间与样本范围
  • 业务场景（经营贷/消费贷等）

输出要求

  • 征信解读摘要（事实）
  • 风险关注点与可能成因（推断需标注）
  • 追问方向与补充资料建议
  • 待核验事项清单

风险与边界

  • 不得把相关性直接写成因果关系
  • 不得把解读结论当作审批结论
  • 数据缺失必须标注并降级处理

信息不足时的处理

  • 输出可解释范围内的事实与趋势
  • 列出缺失字段并给出补充建议

配套脚本

  • scripts/credit_bureau_interpretation.py：读取征信摘要与阈值配置，输出关注点与追问方向。

脚本使用

python scripts/credit_bureau_interpretation.py --input bureau.json --rules rules.json --output out.json

bureau.json 示例结构

{
  "report_date": "2026-01-15",
  "summary": {
    "inquiries_3m": 6,
    "inquiries_6m": 9,
    "delinquencies_12m": 1,
    "max_dpd_24m": 30,
    "revolving_utilization": 0.78,
    "open_accounts": 12,
    "total_balance": 260000
  }
}

rules.json 示例结构

{
  "thresholds": {
    "max_inquiries_3m": 5,
    "max_utilization": 0.7,
    "max_delinquencies_12m": 0
  }
}

out.json 输出要点

  • flags：命中阈值的风险点
  • interpretation：业务解读摘要
  • questions：建议追问方向
  • missing_fields：缺失字段

交付标准

  • 清楚区分事实、解释与推断
  • 输出可直接用于面谈或补充材料准备