Analytics to Combat Fraudulent Behaviour on Online Platforms

亚马逊、Uber和Spotify等数字服务改变了我们消费商品和服务的方式。在幕后，这些平台依靠数据驱动的策略来应对各种运营挑战，例如，然而，这些算法所做的选择可能会对平台上的服务提供商的收入产生不利影响，这些服务提供商随后被驱使操纵数据以“玩弄系统”。例如：1）亚马逊上的卖家诉诸欺诈性评论和点击机器人来提升自己的地位，2）Spotify上的艺术家雇佣假用户来流媒体播放他们的音乐，因为他们的收入份额与他们的流媒体播放量成正比。数据操纵的兴起有可能破坏对数字经济的长期信任，因为它可能导致数百万消费者和服务提供商的不公平结果。认识到这一点，学术研究人员和平台本身都投入了大量资源用于欺诈检测，例如，2019年，亚马逊斥资5亿美元打击其平台滥用行为。不幸的是，这些努力往往是临时性的，“被动而非主动”。 该提案的重点是在面对数据操纵时为在线平台开发规范性的数据驱动分析。与现有的以欺诈检测为中心的方法不同，该项目通过设计即使在数据损坏时也能学习最佳结果的解决方案来优先考虑鲁棒性。特别是，我们采用了三管齐下的方法，融合了理论和实证方法：1。引入一个新的框架，对平台上的敌对用户进行建模，并量化这些用户的经济影响。2.设计机器学习算法，这些算法对于与消费者选择和收入管理相关的问题具有鲁棒性。3.目前的案例研究有关电子商务，流等，以评估的方法，并得出政策的影响。 最后，所提出的方法将进行评估，对国家的最先进的算法欺诈检测，以表征是否主动的方法优于反应。 影响：仅在加拿大，数字经济在2019年就为整体GDP贡献了1180亿美元。在这种情况下，拟议项目的完成将标志着朝着使数字经济更加可靠和防止不公平结果迈出的重要一步。与此同时，我们的研究还将为政策制定者提供及时了解在线欺诈的社会影响，以及指导公司如何解决这一问题的指导方针。最后，该项目产生的跨学科出版物将推进最先进的技术，为不同级别的HQP培训提供机会，并汇集了运营以外的众多领域，包括经济学，市场营销和计算机科学。

Digital services such as Amazon, Uber, and Spotify have transformed how we consume goods and services. Behind the scenes, these platforms rely on data-driven policies to tackle various operational challenges, e.g., matching demand and supply, pricing, etc. However, the choices made by these algorithms can adversely impact the revenue of the service providers on the platform, who are then driven to manipulate the data in order to "game the system". For instance: 1) sellers on Amazon resort to fraudulent reviews and click bots to boost their own standing, 2) artists on Spotify hire fake users to stream their music as their revenue-share is proportional to their streaming volume. The rise of data manipulation threatens to undermine long-term trust in the digital economy as it can result in less-than-equitable outcomes for millions of consumers and service providers. Recognizing this, both academic researchers and the platforms themselves have devoted considerable resources to fraud detection, e.g., in 2019, Amazon spent $500 million to combat the abuse of its platform. Unfortunately, these efforts tend to be ad-hoc and "reactive rather than proactive". This proposal focuses on developing prescriptive, data-driven analytics for online platforms in the face of data manipulation. In a departure from existing approaches that center around fraud detection, this project prioritizes robustness by designing solutions that learn the optimal outcome even when the data is corrupted. In particular, we adopt a three-pronged approach that blends theoretical and empirical methodologies: 1. Introduce a new framework for modelling adversarial users on platforms and quantify the economic impact of such users. 2. Design machine learning algorithms that are robust to manipulation for problems relating to consumer choice and revenue management. 3. Present case studies pertaining to e-commerce, streaming, etc. to evaluate the approach and derive policy implications. Finally, the proposed methods will be evaluated against state-of-the-art algorithms for fraud detection to characterize whether a proactive approach outperforms reactive ones.   Impact: In Canada alone, the digital economy contributed $118 billion to the overall GDP in 2019. In this context, the completion of the proposed project would mark a significant step towards making the digital economy more reliable and preventing unfair outcomes. At the same time, our research will also provide policy makers with a timely understanding of the societal implications of online fraud, and guidelines on how to direct companies to address this problem. Finally, the interdisciplinary publications resulting from this project would advance the-state-of-the-art, provide opportunities for training HQP at different levels, and bring together numerous domains beyond Operations including Economics, Marketing and Computer Science.