Fighting Insurance Fraud with Hybrid AI/ML Models: Discuss the Potential for Combining Approaches for Improved Insurance Fraud Detection
Fighting Insurance Fraud with Hybrid AI/ML Models: Discuss the Potential for Combining Approaches for Improved Insurance Fraud Detection
复制标题
使用混合 AI/ML 模型打击保险欺诈：讨论改进保险欺诈检测的组合方法的潜力
DOI：
10.1109/c2i659362.2023.10431155
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
J.Logeshwaran
中科院分区：
文献类型：
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作者：
Venkata Ramana Saddi;Technology Lead;Swetha Boddu;Bhagawan Gnanapa;J.Logeshwaran
AI/ML混合时尚的出现，为保险企业内部的高级欺诈检测提供了可能。结合几种AI/ML方法，包括监督和非监督学习、深度学习和草药语言处理，可以提供一套有效的工具来打击欺诈性索赔。有监督的了解可以发现索赔事实中可能表明欺诈的模式，而无监督的获取知识可以提醒行为的意外变化或行为的离群值。深度掌握可以分析海量信息以发现可疑索赔风格，草药语言处理可以快速查找可疑关键字的海量信息单元。混合AI/ML时尚可以帮助感知甚至最先进的欺诈操作，并在异常变得太大而无法操纵之前偶然发现它们。这些时尚还可以在欺诈发展变得太大之前利用它们，考虑到早期预防方法。通过利用不同的AI/ML策略，保险公司能够更高地保护自己免受欺诈行为的影响，并最大限度地提高其欺诈检测策略的效率。
The emergence of hybrid AI/ML fashions has allowed for advanced fraud detection within the insurance enterprise. Combining a couple of AI/ML methods including supervised and unsupervised studying, deep studying, and herbal language processing, can offer a effective set of tools to hit upon fraudulent claims. Supervised getting to know can discover patterns in claims facts that might indiciate fraud, while unsupervised gaining knowledge of can alert to surprising changes in behavior or outliers in behavior. Deep mastering can analyze enormous quantities of information to discover suspicious claims styles, and herbal language processing can speedy seek massive information units for suspicious keywords. Hybrid AI/ML fashions can help perceive even the most state-of-the-art fraud operations and stumble on anomalies before they grow to be too massive to manipulate. these fashions also can be leveraged to hit upon fraud developments before they become too full-size, making an allowance for early prevention methods. by way of leveraging disparate AI/ML strategies, insurers are higher able to shield themselves from fraudulent conduct and maximize the efficiency of their fraud detection tactics..