freedakgmail
79 lines
2.6 KiB
Python
79 lines
2.6 KiB
Python
"""场景一 · 政企收入全链路穿透：拆单规避检测（R8）。
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检测点：
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1. 合同金额集中分布在审批阈值边缘（如阈值 80% 以上但不超阈值）。
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2. 结合知识图谱穿透识别隐性实控人（多个客户经法人关联到同一实控人）。
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满足上述模式则生成线索，附证据链与人话理由。
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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@dataclass
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class ContractRecord:
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"""穿透分析输入：一份合同的关键信息。"""
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contract_id: str
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customer_key: str
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amount: float
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@dataclass
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class SplitFinding:
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"""拆单检测结果。"""
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near_threshold: list[ContractRecord] = field(default_factory=list)
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ratio: float = 0.0
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total_amount: float = 0.0
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@property
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def hit(self) -> bool:
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return len(self.near_threshold) >= 3
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def detect_threshold_edge(
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contracts: list[ContractRecord],
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approval_threshold: float,
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edge_ratio: float = 0.8,
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) -> SplitFinding:
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"""识别金额集中在审批阈值边缘 [edge_ratio*阈值, 阈值) 的合同。
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这类"刚好低于阈值"的批量合同是典型的拆单规避特征。
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"""
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if approval_threshold <= 0:
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raise ValueError("审批阈值必须为正数")
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lower = edge_ratio * approval_threshold
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near = [c for c in contracts if lower <= c.amount < approval_threshold]
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finding = SplitFinding(
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near_threshold=near,
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ratio=(len(near) / len(contracts)) if contracts else 0.0,
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total_amount=sum(c.amount for c in near),
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)
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return finding
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def split_risk_score(finding: SplitFinding, shared_controller: bool) -> float:
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"""综合评分：阈值边缘集中度 + 是否穿透到同一实控人。"""
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if not finding.hit:
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return 0.0
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base = min(0.6, 0.1 * len(finding.near_threshold)) # 数量越多越可疑
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base += 0.2 * finding.ratio
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if shared_controller:
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base += 0.3 # 同一实控人是强证据
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return round(min(base, 1.0), 3)
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def build_rationale(finding: SplitFinding, threshold: float, shared_controller: bool) -> str:
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parts = [
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f"检测到 {len(finding.near_threshold)} 份合同金额集中在审批阈值 "
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f"{threshold:.0f} 的边缘区间（占比 {finding.ratio:.0%}），",
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f"边缘合同金额合计约 {finding.total_amount:.0f}。",
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]
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if shared_controller:
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parts.append("且经工商关联穿透，相关客户疑似同属一个隐性实控人，高度符合拆单规避特征。")
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else:
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parts.append("建议进一步穿透客户关联关系以确认是否同一实控人。")
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return "".join(parts)
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