用无监督学习自动检测寿险合同异常，解决数据标注少难题。
A Machine Learning-based Anomaly Detection Framework in Life Insurance Contracts
- 采用经典与现代无监督方法检测合同异常
- 在两个数据集上验证了方法有效性
- 流程自动化，非专业人员也能使用
寿险业务依赖海量数据，其商业模式基于公司收取保费并承诺在事故时提供赔付。因此，数据库中数据的完整性至关重要。确保数据可靠性的一种方法是自动检测异常。尽管该方法极具价值，但因正常与异常合同或交互的标注数据稀缺，实现起来颇具挑战。本文探讨了多种经典与现代的无监督异常检测方法，并在两个不同数据集上比较了它们的性能。为促进企业采纳，本研究还探索了流程自动化路径，使非数据科学家也能轻松应用。
原文摘要 · Abstract (English)
Life insurance, like other forms of insurance, relies heavily on large volumes of data. The business model is based on an exchange where companies receive payments in return for the promise to provide coverage in case of an accident. Thus, trust in the integrity of the data stored in databases is crucial. One method to ensure data reliability is the automatic detection of anomalies. While this approach is highly useful, it is also challenging due to the scarcity of labeled data that distinguish between normal and anomalous contracts or inter\-actions. This manuscript discusses several classical and modern unsupervised anomaly detection methods and compares their performance across two different datasets. In order to facilitate the adoption of these methods by companies, this work also explores ways to automate the process, making it accessible even to non-data scientists.
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