改进焦点损失函数，提升保险欺诈检测中少数类识别能力。
An Enhanced Focal Loss Function to Mitigate Class Imbalance in Auto Insurance Fraud Detection with Explainable AI
- 三阶段训练：凸代理损失初始化，非凸损失增强特征区分，标准焦点损失优化
- 在真实数据集上，少数类F1分数和AUC均优于传统方法
- 结合SHAP分析实现可解释性，适合需要透明决策的保险风控场景
检测汽车保险欺诈仍是一项具有挑战性的分类任务，主要源于正常与欺诈案例之间的极端不平衡。标准学习算法易过度拟合多数类，导致对经济影响显著的少数事件检测效果差。本文提出一种结构化的三阶段训练框架，包含凸代理焦点损失以实现稳定初始化，可控非凸中间损失以提升特征区分能力，以及标准焦点损失以细化少数类敏感性。推导了代理损失在预测空间保持凸性的条件，并展示了其与深度序列模型结合时能实现更可靠的优化。基于一个专有汽车保险数据集，所提方法在少数类F1分数和AUC上均优于传统的焦点损失训练和重采样基线。该方法还通过SHAP分析提供可解释的特征归因模式，适用于精算与反欺诈分析。
原文摘要 · Abstract (English)
Detecting fraudulent auto-insurance claims remains a challenging classification problem, largely due to the extreme imbalance between legitimate and fraudulent cases. Standard learning algorithms tend to overfit to the majority class, resulting in poor detection of economically significant minority events. This paper proposes a structured three-stage training framework that integrates a convex surrogate of focal loss for stable initialization, a controlled non-convex intermediate loss to improve feature discrimination, and the standard focal loss to refine minority-class sensitivity. We derive conditions under which the surrogate retains convexity in the prediction space and show how this facilitates more reliable optimization when combined with deep sequential models. Using a proprietary auto-insurance dataset, the proposed method improves minority-class F1-scores and AUC relative to conventional focal-loss training and resampling baselines. The approach also provides interpretable feature-attribution patterns through SHAP analysis, offering transparency for actuarial and fraud-analytics applications.
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