Financial Fraud Prevention via Machine Learning
Modern technologies such as EMV (the chip card) have greatly reduced fraudulent card transactions at brick and mortar stores. Unfortunately, online credit card fraud remains prevalent and is projected to cost consumer over $32 billion by 2020 . In this session we will discuss a how data science is used at Bank of America to reduce the risk of fraudulent transactions. In particular, we will explore a machine learning model that uses customer behavior history to detect suspicious logins in real time for over 20 million logins a day.
Ali Raza is a Machine Learning Engineer at Bank of America where he works within the consumer protection department. Prior to Bank of America, Ali was as a Software Engineer at Cerner Corporation working on building Electronic Health Records for the medical industry. Ali holds a bachelor’s degree in Computer Science from the University of Missouri.