This post was cowritten by Ziv Pollak, Machine Learning Team Lead, and Sarvi Loloei, Machine Learning Engineer at Clearly. The content and opinions in this post are those of the third-party authors and AWS is not responsible for the content or accuracy of this post. A pioneer in online shopping, Clearly launched their first site in 2000. Since then, we've grown to become one of the biggest online eyewear retailers in the world, providing customers across Canada, the US, Australia, and New Zealand with glasses, sunglasses, contact lenses, and other eye health products. Through its mission to eliminate poor vision, Clearly strives to make eyewear affordable and accessible for everyone.

Online retail, eCommerce, frequently falls victim to fraud conducted by malicious customers (fraudsters) who obtain goods or services through deception. Fraud coordinated by groups of professional fraudsters that place several fraudulent orders to maximize their gain is referred to as organized fraud. Existing approaches to fraud detection typically analyze orders in isolation and they are not effective at identifying groups of fraudulent orders linked to organized fraud. These also wrongly identify many legitimate orders as fraud, which hinders their usage for automated fraud cancellation. We introduce a novel solution to detect organized fraud by analyzing orders in bulk. Our approach is based on clustering and aims to group together fraudulent orders placed by the same group of fraudsters. It selectively uses two existing techniques, agglomerative clustering and sampling to recursively group orders into small clusters in a reasonable amount of time. We assess our clustering technique on real-world orders placed on the Zalando website, the largest online apparel retailer in Europe1. Our clustering processes 100,000s of orders in a few hours and groups 35-45% of fraudulent orders together. We propose a simple technique built on top of our clustering that detects 26.2% of fraud while raising false alarms for only 0.1% of legitimate orders.

New Delhi: E-commerce companies are focusing on artificial intelligence (AI) and virtual reality with a view to cut logistics costs and identify fraudulent orders, said a report by global auditing and consulting firm PwC. With an emerging middle-class population of more than 500 million and approximately 65% of the population aged 35 or below, India represents a highly aspirational consumer market for retailers across the globe, said the PwC TechWorld report. "E-commerce players are revamping their technology strategies to maintain their competitive edge. Most e-commerce platforms are upping their investments in areas such as conversational commerce, artificial intelligence (AI), virtual reality (VR)/augmented reality (AR) and analytics technologies," it said. It observed that to identify fraudulent orders, reduce return rate and also cut down on logistics cost, e-commerce companies are investing in robotics and AI heavily.

PetSmart is catching criminals through high-tech methods, and last year alone saved $12 million by pinpointing fraudulent orders before they were shipped. This year, the retailer is on track to match that figure and then some. This ebook, based on the latest ZDNet/TechRepublic special feature, looks at the rise of e-commerce and the digital transformation of retail companies. As retail gets more high-tech, it's only natural that fraud prevention has more technology added to catch the criminals who try to place fraudulent orders. PetSmart, and many other retailers, are using technology from Kount to take fraud prevention to the next level and not just stop losses at the stores, but also help authorities prosecute criminals.