Refund fraud analytics for an online retail purchases

Abstract

Online shopping is growing fast across the globe and so are its complexities. Fraud is a complicated phenomenon and its mitigation is critical for running a smooth business. The case considered for the present study is fraud mitigation in return–refund process managed by the customer services of an online retail business. Predictive analytics approach was used to identify early indicators of agent refund fraud–a rare event. The technique used to solve the problem was a Penalised Likelihood based Logistic Regression model. The proposed model allowed the business to select top 5% sample of refund transactions with a higher likelihood of fraud as indicated and queue them for an audit. Implementation of this model resulted in an incremental lift in fraud capture rate.