Machine Learning and Large Scale Data Analysis On Centralized Platform
Who is this presentation for?business leader, engineers, data scientists
As the global retail leader in the world, Wal-Mart served nearly 270 million customers a week in 2018 .Tremendous data is collected and flows into Wal-Mart data ecosystem. Data is comprised of store, clubs, online, digital purchases, customer services (e.g., return) and financial services (e.g., money transfer). Like many other big companies, Wal-Mart has an enterprise data hub. But how to leverage data resources and how to connect customers’ behaviors from different platforms is an interesting and challenging topic.
A group of data scientists and engineers from Wal-Mart will share their knowledge and successful story about mining information from different data sources and connect customers’ activities to provide secure and seamless shopping experience. In this talk, they will focus on sharing the design of centralized risk/abuse management platform, and how this highly sophisticated platform enables dynamic and complex analytics of large-scale data from different domains. They will also share a study of protecting customer account through linking customer behaviors in their purchases, returns and financial services.
1. An introduction about Wal-Mart risk/abuse management platform;
2. Risk and abuse problems in Wal-Mart ecosystem;
3. The data-driven analytics and advanced machine learning algorithm to defend against fraud/abuse;
4. Case studies of customer account protection;
Prerequisite knowledgeFamiliarity with large-scale datasets and systems, data mining and machine learning technologies.
What you'll learn
James Tang is a senior director of engineering at Wal-Mart Labs. He has spent time in creating large-scale, resilient, and distributed architectures with high-security and high-performance for enterprise applications, web applications, online payments, online games, and real-time predictive analytics applications. While enthusiastic about technologies, he enjoys mentoring, training and leading teams to be successful with distributed systems concepts, micro-services, DevOps, and cloud-native application design.
Yiyi Zeng is a senior manager/principal data scientist at Wal-Mart Labs. She has 12 years of extensive experience in business analytics and intelligence, decision management, fraud detection, credit risk, online payment and e-commerce across various business domains including both Fortune 500 firms and startups. She and her team use supervised and unsupervised machine learning technics to detect frauds including stolen financial, account take over, identity fraud, promotion/return abuse & victim scam. She is enthusiastic about mining large-scale data and applying machine learning knowledge to improve business outcomes.
Linhong Kang is a manager/staff data scientist at Wal-Mart Labs. She has more than 10 years of experience in data science, business analytics, and risk/fraud management across different industries including business consulting, bank, financial payment and ecommerce. She is the lead of multiple fraud/abuse detection solutions for Wal-Mart’s various products. She is passionate about translating business problems into qualitative questions, delivering cost-savings and helping companies to become more profitable.
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