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Detecting and Reducing Cost of Fraud Rings
技术
- 应用基础设施与中间件 - 数据交换与集成
适用行业
- 金融与保险
适用功能
- 商业运营
挑战
一家领先的保险公司希望减少数十亿美元的汽车保险索赔支出，这是其最大的年度支出。目前，该公司仅避免支付预计 10% 的欺诈性索赔中的约 0.33%。保险公司使用手动 (60-65%) 和自动系统 (30-35%) 来标记可疑索赔。这些索赔被传递给调查案件经理，他们阅读调整员和其他成员的笔记，探索欺诈观察名单，并搜索网络。他们现有的方法无法轻易发现欺诈圈，更重要的是，无法发现其他欺诈圈之间的勾结。此外，调查小组通常会在 30 天或更短时间内决定是支付还是拒绝索赔。这是由于繁重的案件量和对索赔处理快速周转的期望，以使客户满意。
客户
未公开
关于客户
美国领先的保险公司
解决方案
自然智能平台在过去三年中从三个州摄取了数据，包括汽车伤害索赔（结构化数据）和 MSR 笔记（非结构化数据）。为每个索赔创建复合记忆，并使用相似性分析为保险公司提供他们以前未见过的数据的洞察力。藏红花的联想记忆从遗留声明和新声明中“学习”；这使调查团队能够查看所有索赔中提供者的模式和相似性，并轻松识别欺诈圈。例如，Saffron 发现调查观察名单中的一家放射诊所实际上是一个更大的欺诈团伙的一部分，并且是一个将三个潜在欺诈团伙连接在一起的公共链接。该诊所此前因 MRI 超额收费而被认定为欺诈，但调查小组无法查看该诊所与其他提供者的可能联系。使用 Saffron 的“相似性原因”分析——其中每个索赔（和相关行为者）都有一个独特的个人签名——Saffron 的自然智能平台阐明了与该特定诊所以及其他勾结的圈子的所有隐藏联系。
收集的数据
Insurance Claims
运营影响
数量效益
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