Nonstandard insurers suffer from a peculiar variant of fraud wherein an overwhelming majority of claims have the semblance of fraud. We show that state-of-the-art fraud detection performs poorly when deployed at underwriting. Our proposed framework "FraudBuster" represents a new paradigm in predicting segments of fraud at underwriting in an interpretable and regulation compliant manner. We show that the most actionable and generalizable profile of fraud is represented by market segments with high confidence of fraud and high loss ratio. We show how these segments can be reported in terms of their constituent policy traits, expected loss ratios, support, and confidence of fraud. Overall, our predictive models successfully identify fraud with an area under the precision-recall curve of 0.63 and an f-1 score of 0.769.

中文翻译：

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FraudBuster：减少汽车保险市场中的欺诈。

非标准保险公司遭受欺诈的一种特殊形式，其中绝大多数索赔具有欺诈的外观。我们显示，在承销商部署时，最新的欺诈检测性能不佳。我们提议的框架“ FraudBuster”代表了一种新的范例，以一种可解释且符合法规的方式预测承销时的欺诈行为。我们表明，欺诈行为最具可操作性和普遍性的特征是对欺诈行为具有高度信心和高损失率的细分市场。我们展示了如何根据其组成政策特征，预期损失率，支持和欺诈信心对这些细分进行报告。总体而言，我们的预测模型成功识别出欺诈行为，其精确召回曲线下的面积为0.63，f-1得分为0.769。