Forecasting and influencing product returns and fraud rates in a Covid-19 world

新型冠状病毒肺炎（COVID-19，即2019冠状病毒病）显著加剧了产品退货率高的问题，而退货率在过去几年一直在上升。这对零售商和社会都是一个重大挑战，造成经济、社会和生态危害。退货导致了复杂的处理、运输和资源浪费，因为许多产品无法转售，只能进入垃圾填埋场。封锁期间，网上购物蓬勃发展，许多零售商延长了退货期。当非必需品零售商重新开业时，产品退货激增。在正常时期成本高昂的问题（如衣柜、欺诈性退款、连环杀手）在这一流行病时期变得更糟。最近的研究显示，许多顾客将保留他们新的网上购物习惯，问题也将继续存在。我们将进行消费者调查，访问25家零售商，并与5家零售商密切合作（附支持信），以提高我们对流行病中消费者行为的了解。然后，我们将使用可解释的人工智能（AI）技术在微观和宏观层面对此进行建模，以预测在新冠肺炎疫情主导的世界中的回报率和欺诈率。为了缓解这一问题，我们将制定一系列措施，说明预期的效果和环境影响。最终目标是帮助零售商在这一充满挑战的时期高效运营并蓬勃发展，避免裁员，从而增加福利的财政负担。该项目将独特地将联合收割机行为研究与可解释的人工智能的开发相结合，零售商可以使用该人工智能来减轻产品退货的经济和生态影响。2020年b），许多零售商已经开始做出改进，并通过执行现有的退货政策来处理退货率，欺诈和相关的退货成本。然而，新型冠状病毒肺炎（COVID-19）否定了这一成功的大部分。零售商最近报告说，产品退货率增加，相关欺诈行为增加，以及新的欺诈类型。这消耗了很大一部分资源，而此时零售商仍在努力从长期关闭的商店中恢复过来，在新的限制下运营（例如试衣间关闭和退回的产品需要隔离）以及当地封锁的威胁。此外，个人流动性受到限制，加上恐惧和不喜欢戴口罩，导致网上购物急剧增加，which哪一个typically典型leads导致tomore更多returns返回and more fraud欺诈.此外，经济和社会危机正在导致很大一部分人的财务和心理挣扎加剧，这反过来又增加了欺诈的可能性。需要评估这种情况对环境和经济的影响，并确定减轻影响的方法。这一挑战解决了零售商的需求，在最近的讨论会上与ECR社区，主要研究者参加，并与研究差距之前确定的Frei等人。（2020 a，B）。

Covid-19 has significantly aggravated the problem of high product returns rates, which havebeen increasing over the last few years. This is a significant challenge for retailers andsociety, causing economic, social and ecological harm. Returns lead to added complexprocessing, transportation and wasted resources, as many products cannot be resold andrisk going to landfill.Online shopping thrived during the lockdown, and many retailers extended their returnsperiods. A surge of product returns arrived when non-essential retailers reopened. Problemsthat are costly in normal periods (e.g. wardrobing, fraudulent refunds, serial returners) havebecome worse in this pandemic period. With recent research showing that many customerswill retain their new online shopping habits, the problems will stay, too.We will conduct a consumer survey, interview 25 retailers and work closely with 5 retailers(letter of support attached) to improve our understanding of consumer behaviours in apandemic. We will then model this at micro and macro levels using explainable artificialintelligence (AI) techniques to forecast returns and fraud rates in a world dominated byCovid-19 conditions. To mitigate this, we will develop a set of measures, indicating theexpected effectiveness and environmental impact.The ultimate goals are to help retailers operate efficiently and thrive in this challenging time,avoiding the need to cut jobs and thereby increasing the financial burden on welfare. Thisproject will uniquely combine behavioural research with the development of explainable AIthat retailers can use to mitigate the economic and ecological effects of product returns.Following the recommendations resulting from our previous work on the costs andvulnerabilities of product returns processes (Frei et al., 2020b), many retailers had startedto make improvements and get a handle on returns rates, fraud and associated costs ofreturns through enforcing their existing returns policies. However, Covid-19 has negatedmost of this success. Retailers have recently reported on increased product returns ratesand increased related fraud as well as new fraud types. This consumes a large proportionof resources, just when retailers are still struggling to recover from a long period of shopclosures, operating under new constraints (e.g. fitting rooms closed and returned productsneeding quarantine) and with the threat of local lockdowns.Furthermore, the restriction of individual mobility in combination with fear and a dislike ofwearing masks have led to a sharp increase in online shopping, which typically leads tomore returns and more fraud. Furthermore, the economic and social crisis is leading toincreased financial and psychological struggles in a large percentage of the population,which in turn increases the likelihood of fraud.The environmental and economic effects of this situation need to be assessed, and waysto mitigate them need to be defined. This challenge addresses the needs of retailers,expressed in recent discussion sessions with the ECR Community that the principalinvestigator attended, and aligns with the research gaps previously identified in Frei et al.(2020a,b).