58 lines
2.3 KiB
Python
58 lines
2.3 KiB
Python
"""Rule-based fraud amount assessment.
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Classifies each transaction into high / medium / low confidence fraud,
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and generates initial reason text.
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"""
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from app.models.transaction import TransactionRecord
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from app.models.assessment import ConfidenceLevel
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FRAUD_KEYWORDS = ["投资", "保证金", "手续费", "解冻", "税费", "充值", "提币", "提现"]
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def classify_transaction(tx: TransactionRecord) -> tuple[ConfidenceLevel, str, str]:
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"""Return (confidence_level, reason, exclude_reason)."""
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if tx.is_transit:
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return (
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ConfidenceLevel.low,
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f"该笔为本人账户间中转（{tx.source_app.value} -> {tx.counterparty_name}），不直接计入被骗损失。",
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"本人账户间互转，仅作为资金路径展示。",
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)
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if tx.direction.value == "in":
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return (
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ConfidenceLevel.low,
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f"该笔为收入方向交易（+¥{float(tx.amount):,.2f}），通常不属于被骗损失。",
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"收入交易不计入损失。",
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)
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remark = tx.remark or ""
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counterparty = tx.counterparty_name or ""
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confidence = tx.confidence
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has_fraud_keyword = any(kw in remark or kw in counterparty for kw in FRAUD_KEYWORDS)
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if confidence >= 0.9 and has_fraud_keyword:
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return (
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ConfidenceLevel.high,
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f"受害人经{tx.source_app.value}向「{counterparty}」转账¥{float(tx.amount):,.2f}，"
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f"备注为「{remark}」，与诈骗常见话术吻合，OCR置信度{confidence:.0%}。",
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"",
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)
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if confidence >= 0.85:
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reason = (
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f"受害人经{tx.source_app.value}向「{counterparty}」转账¥{float(tx.amount):,.2f}。"
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)
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if has_fraud_keyword:
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reason += f"备注「{remark}」含涉诈关键词。"
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return ConfidenceLevel.high, reason, ""
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reason += "建议结合笔录确认是否受诱导操作。"
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return ConfidenceLevel.medium, reason, "如经核实该笔为受害人主动日常消费，应排除。"
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return (
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ConfidenceLevel.medium,
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f"受害人经{tx.source_app.value}向「{counterparty}」转账¥{float(tx.amount):,.2f}，"
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f"OCR置信度较低({confidence:.0%})，需人工复核。",
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"OCR置信度不足，可能存在识别误差。",
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)
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