保险欺诈检测：利用数据准确识别欺诈性索赔
公司规模
Large Corporate
地区
- Europe
国家
- France
产品
- Dataiku Data Science Studio (DSS)
技术栈
- Python
- Scikit-Learn
- R
实施规模
- Enterprise-wide Deployment
影响指标
- Cost Savings
- Productivity Improvements
技术
- 分析与建模 - 机器学习
- 分析与建模 - 预测分析
适用行业
- 金融与保险
适用功能
- 商业运营
用例
- 欺诈识别
服务
- 数据科学服务
关于客户
Santéclair 是多家补充健康保险公司的子公司，包括 Allianz、Maaf-MMA、Ipeca Prévoyance 和 Mutuelle Générale de la Police。它们为 1000 多万受益人提供医疗保健服务，帮助支付眼科、牙科和耳科费用以及饮食和整形外科服务。13 年来，Santéclair 已证明其在风险管理方面的专业知识，使 50 多家健康保险公司受益。它们总部位于欧洲，在金融服务行业开展业务。
挑战
保险机构不断面临欺诈风险，包括虚假索赔、虚假账单、不必要的程序、精心策划的事件和隐瞒信息。Santéclair 是几家补充健康保险公司的子公司，一直在努力应对来自验光师和患者的欺诈性报销。他们缺乏一个能够有效分析正确数据并适应日益复杂的欺诈者的系统。相反，他们依靠“if-then-else”业务规则来识别可能的欺诈案件，这导致人工审计团队将时间花在太多低风险案件上。随着报销额的增加（每年超过 150 万），他们需要提高效率和生产力。
解决方案
Santéclair 通过 IMT TeraLab 平台主导的 POC 在 Dataiku 数据科学工作室 (DSS) 中找到了解决方案。数据咨询机构 Eulidia 使用 Dataiku 制作了一种算法，通过向人工审计团队提供很可能是欺诈的案例，帮助他们识别更多欺诈行为。该解决方案涉及使用先进的机器学习算法智胜欺诈者，这些算法不断更新并使用最新数据自动学习或重新训练，以便立即识别和审计任何新的欺诈模式。Dataiku 处理整个工作流程，从原始数据到将预测模型公开给操作应用程序。该解决方案还涉及自动组合来自不同数据集的数百个变量，包括患者/处方者历史记录、交互图、处方特征和其他上下文数据。
运营影响
数量效益
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