Fraud detection and prevention play an important part in ensuring the
sustained operation of any e-commerce business. Machine learning (ML) often
plays an important role in these anti-fraud operations, but the organizational
context in which these ML models operate cannot be ignored. In this paper, we
take an organization-centric view on the topic of fraud detection by
formulating an operational model of the anti-fraud departments in e-commerce
organizations. We derive 6 research topics and 12 practical challenges for
fraud detection from this operational model. We summarize the state of the
literature for each research topic, discuss potential solutions to the
practical challenges, and identify 22 open research challenges.

本文以组织为中心，探讨了反欺诈部门的运营模式，提出了 6 个研究主题和 12 个实践挑战，总结了每个研究主题的文献现状，并讨论了解决实践挑战的潜在解决方案，同时发现了 22 个开放式研究挑战。

电子商务欺诈检测的机器学习：研究议程

Machine Learning for Fraud Detection in E-Commerce: A Research Agenda

Research has a long history of discussing what is superior in predicting
certain outcomes: statistical methods or the human brain. This debate has
repeatedly been sparked off by the remarkable technological advances in the
field of artificial intelligence (AI), such as solving tasks like object and
speech recognition, achieving significant improvements in accuracy through
deep-learning algorithms (Goodfellow et al. 2016), or combining various methods
of computational intelligence, such as fuzzy logic, genetic algorithms, and
case-based reasoning (Medsker 2012). One of the implicit promises that underlie
these advancements is that machines will 1 day be capable of performing complex
tasks or may even supersede humans in performing these tasks. This triggers new
heated debates of when machines will ultimately replace humans (McAfee and
Brynjolfsson 2017). While previous research has proved that AI performs well in
some clearly defined tasks such as playing chess, playing Go or identifying
objects on images, it is doubted that the development of an artificial general
intelligence (AGI) which is able to solve multiple tasks at the same time can
be achieved in the near future (e.g., Russell and Norvig 2016). Moreover, the
use of AI to solve complex business problems in organizational contexts occurs
scarcely, and applications for AI that solve complex problems remain mainly in
laboratory settings instead of being implemented in practice. Since the road to
AGI is still a long one, we argue that the most likely paradigm for the
division of labor between humans and machines in the next decades is Hybrid
Intelligence. This concept aims at using the complementary strengths of human
intelligence and AI, so that they can perform better than each of the two could
separately (e.g., Kamar 2016).

探讨了人类智能与人工智能在预测特定结果方面的优劣，提出了在未来几十年内的劳动分工典范可能是混合智能。这个概念旨在利用人类智能和人工智能的互补优势，以使它们能够比两者分别表现得更好，并探讨了其在组织上下文中的应用。