Using big data analytics to combat retail fraud

Fraudulent returns are seen as a misfortune for most retailers because it reduces sales and induce greater costs and challenges in returns management. While extant research suggests one of the causes is retailers’ liberal return policies and that retailers should restrict their policies, there is no study systematically exploring the impacts of various return policies and fraud interventions on reducing different types of fraudulent behaviour and the costs and benefits of associated interventions. In this paper, we first undertook semi-structured interviews with retailers in the UK and North America to gain insights into their fraud intervention strategies, as well as conducted literature review on fraudulent returns to identify the influential factors that lead customers to return products fraudulently. On this basis, we developed a simulation model to help retailers forecast fraudulent returns and explore how different combinations of interventions might affect the cases of fraudulent returns and associated financial impacts on profitability. The background literature on fraudulent returns, the findings of interviews, and the demonstration and implications of the model on reducing fraudulent returns and related financial impacts are discussed. Our model allows retailers to make cost- effective evaluations and adopt their fraud prevention strategies effectively based on their business models.