RADAR项目：有人类参与的智能早期欺诈检测系统

Project RADAR: Intelligent Early Fraud Detection System with Humans in the Loop

Uber is a worldwide marketplace of services, processing thousands of monetary transactions every second. As a marketplace, Uber takes on all of the risks associated with payment processing. Uber partners who use the marketplace to provide services are paid for their work even if Uber was unable to collect the payment. Fraud response is thus a very important operational component of Uber’s global marketplace.

Uber是一个世界性的服务市场，每秒钟处理成千上万的货币交易。作为一个市场，Uber承担了所有与付款处理有关的风险。使用市场提供服务的Uber合作伙伴，即使Uber无法收取货款，也会得到他们的工作报酬。因此，欺诈应对是Uber全球市场的一个非常重要的运营组成部分。

Industry-wide, payment fraud losses are measured in terms of the fraction of gross amounts processed. Though only a small fraction of gross bookings, these losses impact profits significantly. Furthermore, if a fraudulent activity is not discovered and mitigated immediately, it could soon be further exploited, resulting in serious losses for the company. These dynamics make early fraud detection vital to the company’s financial health.

在整个行业中，支付欺诈的损失是以处理的总金额的比例来衡量的。虽然只是总预订量的一小部分，但这些损失对利润影响很大。此外，如果欺诈活动没有被立即发现和缓解，它可能很快被进一步利用，导致公司的严重损失。这些动态变化使得早期欺诈检测对公司的财务健康至关重要。

Modern fraud detection systems are a combination of classic 1980s AI (also known as an “expert system”) and modern machine learning. We would like to share the journey on how we build the best-in-class automatic fraud detection system and process, leveraging both machine algorithms and human knowledge.

现代欺诈检测系统是20世纪80年代经典的人工智能（也被称为 "专家系统"）和现代机器学习的结合。我们想分享一下我们如何利用机器算法和人类知识，建立一流的自动欺诈检测系统和流程的历程。

Explainability Of Decisions

决策的可解释性

It’s important to understand the value of expert-driven rules in fraud detection. Explainability is of paramount importance when it comes to fraud detection. Uber plays the role of the societal operating system. Incorrect fraud-related decisions can be disruptive not just to individual users, but to whole communities.

了解专家驱动的规则在欺诈检测中的价值很重要。谈到欺诈检测，可解释性是最重要的。Uber扮演的是社会操作系统的角色。与欺诈有关的错误决定不仅对个人用户，而且对整个社区都会产生破坏性影响。

Fraud cannot be stopped by some black-box AI algorithms. The reliance on the engagement of...