In an era where digital transactions are ubiquitous, safeguarding against fraudulent activities is paramount. Join our webinar, "Fraud Detection using Machine Learning," as we explore the dynamic intersection of machine learning and fraud detection. This session is tailored for professionals seeking to fortify their understanding of how machine learning techniques can be harnessed to combat fraud effectively. Whether you're in finance, e-commerce, or any industry dealing with transactions, this webinar equips you with the knowledge to deploy cutting-edge tools in the battle against fraudulent activities.
Agenda for the session
- Introduction to Fraud Detection and Machine Learning
- Key Techniques in Fraud Detection Using Machine Learning
- MIT IDSS' Data Science & Machine Learning Program
- Live Q&A
About Speakers
Vincent Koc
General Manager of Data, hipages Group
Vincent Koc is a highly accomplished, commercially focused technologist with a wealth of experience in data-driven disciplines. Currently, Vincent serves as the Head of Data for the publicly listed company hipages Group (ASX:HPG), where he oversees the data department and data strategy for the group. He holds a fellowship at the Institute of Managers and Leaders Australia, where he serves as a thought leader and mentor to the next generation of data professionals.
Data Science and Machine Learning: Making Data-Driven Decisions Program
The Data Science and Machine Learning: Making Data-Driven Decisions Program has a curriculum carefully crafted by MIT faculty to provide you with the skills & knowledge to apply data science techniques to help you make data-driven decisions.
This data science program has been designed for the needs of data professionals looking to grow their careers and enhance their data science skills to solve complex business problems. In a relatively short period of time, the program aims to build your understanding of most industry-relevant technologies today such as machine learning, deep learning, network analytics, recommendation systems, graph neural networks, and time series.
