Payoneer 案例研究
公司规模
Large Corporate
国家
- Worldwide
- United States
产品
- Iguazio data science platform
- Payoneer digital payment platform
技术栈
- Machine Learning
- Predictive Analytics
- Real-Time Data Collection
- Python
- Spark
实施规模
- Enterprise-wide Deployment
影响指标
- Customer Satisfaction
- Cost Savings
- Digital Expertise
技术
- 分析与建模 - 机器学习
- 分析与建模 - 预测分析
- 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
- 零售
适用功能
- 商业运营
- 销售与市场营销
用例
- 欺诈识别
- 质量预测分析
- 实时定位系统 (RTLS)
服务
- 数据科学服务
- 系统集成
关于客户
Payoneer 的数字平台简化了来自 200 个国家和地区的数百万小型企业、市场和企业的全球商务。他们的创新端到端平台使企业能够扩展其产品和服务并接触全球客户。因此，全球数百万个人和企业依靠 Payoneer 进行跨境支付和收款。面对日益危险的网络安全形势以及欺诈和洗钱活动的普遍性，Payoneer 提出了一项创新举措来确保客户的资金安全。
挑战
数字支付平台 Payoneer 需要一种方法，根据最新的实时数据为其 AI/ML 欺诈预测/预防模型提供服务，以便为客户提供更安全的支付体验。该公司采用的是一种事后检测欺诈企图的追溯方法，这意味着客户只能在欺诈企图（可能成功）后阻止用户。这种方法有几个局限性，包括无法跟踪复杂网络中的欺诈企图、缺乏高级分析和日志关联来识别异常，以及对客户体验和满意度产生负面影响。Payoneer 需要一种解决方案，利用复杂的算法来跟踪多个参数并检测复杂网络中的欺诈行为。虽然 Payoneer 已经建立了复杂的机器学习模型，但这些模型只能离线工作，无法用于应对实时威胁。
解决方案
Payoneer 选择 Iguazio 来实现其最智能的数据科学策略。数据科学平台允许 Payoneer 实时部署和运行其预测机器学习模型。这产生了一个可扩展且可靠的欺诈预测和预防工具，可以实时分析新数据并适应新威胁，从而使 Payoneer 几乎不可能遭受欺诈攻击。Payoneer 部署了 Iguazio 来支持其预测 ML 模型，并根据新数据实时为它们提供服务。这有助于持续监控可疑模式并以最低误报率预防欺诈。生产管道基于通过 Nuclio 函数和 Spark 框架运行的 Python 代码。这种方法不需要将代码从 Python 转换为 Java 或其他语言。Python 一直用于实时部署。
运营影响
数量效益
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