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Major Financial Institution Enhances Anti-Money Laundering Capabilities with TigerGraph
技术
- 分析与建模 - 机器学习
- 网络安全和隐私 - 身份认证管理
适用行业
- 金融与保险
用例
- 欺诈识别
服务
- 系统集成
挑战
该金融机构正在寻求提高其反洗钱网络和链接分析能力，以通过三种方式应用：
- 应识别感兴趣的情况下开放工作项（例如以前的 SAR 文件和其他开放工作项）之间的联系，并提供给分析员和调查员；
- 对实体进行彻底的临时审查应显示围绕特定调查起点的生态系统的联系；
- 该系统应该使分析人员能够识别哪些连接和感兴趣的情况会导致富有成效的调查，并为工作项的创建、休眠或升级提供信息。
该公司检查了许多替代方案，希望找到一种能够提供以客户为中心的方法、最先进的技术以及与现有工作流程无缝集成的下一代数据库管理解决方案。
客户
主要银行
关于客户
该公司为个人、企业、政府实体和其他金融机构提供银行、投资、抵押、信托和支付服务产品。它是美国最大的银行机构之一，拥有 3,000 多家分支机构，主要分布在美国西部和中西部。该公司拥有子公司，其中包括为商家处理信用卡交易的处理器和向金融机构发行信用卡产品的信用卡发行商。
解决方案
TigerGraph 为这家金融机构提供了执行深入分析的能力，这在其基于关系数据库的先前遗留系统中是不可能的。 TigerGraph 为该企业提供了在上下文中查看其连接数据的能力。此外，TigerGraph 正在提供可扩展性来分析不断增加的数据量和可扩展性，这源于其对机器学习的支持，使银行的反洗钱计划领先于金融犯罪分子。
Pagantis 在 AWS 上部署了 TigerGraph，其结果是一个可扩展的高性能系统，使 Pagantis 能够快速提供对复杂的基于关系的工作流程的实时洞察，这些工作流程在信用评分、欺诈检测和推荐引擎等任务中很常见，以及风险分析。
运营影响
相关案例.
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