Machine Learning for Fraud Prevention

Step into the future of eCommerce fraud fighting with our course on Machine Learning for Fraud Prevention. Designed for fraud and risk professionals in the digital age, this course offers a deep dive into the essentials of machine learning (ML) and its pivotal role in combating card-not-present fraud. You'll learn to define machine learning, differentiate various model types, and understand their specific applications in fraud management. Additionally, you'll learn about model scores, including the distinction between raw and calibrated scores, and how to set decision thresholds. This course shows how machine learning can be combined with rules and manual review for optimal decision-making in fraud prevention. Ideal for those looking to harness the power of machine learning in securing eCommerce platforms.

Who Should Enroll?

• Fraud prevention professionals looking to integrate machine learning techniques into their fraud-fighting strategies.
• Risk management professionals seeking advanced methods to identify and mitigate potential fraud risks.
• Data analysts and scientists interested in applying their expertise to detect and prevent fraud through machine learning models.
• Financial and payment professionals seeking to improve their knowledge of advanced fraud prevention techniques.
• This course may also benefit eCommerce business owners and managers, IT and security professionals, and compliance officers in digital retail.

Learning Objectives

• Define machine learning
• Distinguish types of models and their applications in fraud management
• Compare and contrast the advantages and disadvantages between the traditional approach to fraud fighting and machine learning
• Convert data into features that a model can use
• List methods to label transactions as fraudulent or not
• Deconstruct the components of a model
• Identify key metrics for evaluating machine learning models
• Examine common types of drift that can impact a model’s performance
• Define model scores
• Differentiate between raw scores and calibrated scores
• Explain decision thresholds
• Summarize how thresholds can be combined with rules and manual review to make decisions

Program Details

• Program Level: Basic
• Program Field of Study: Specialized Knowledge
• Program Delivery Method: QAS Self Study
• CPE Credits: 2.5
• Advanced Preparation and/or Pre-requisites: None
• Pricing: USD $300 (plus applicable taxes) | MRC Members receive a 50% discount on all eLearning courses.

Please allow approx. 3 hours to complete this course. The course culminates with a comprehensive final assessment to evaluate the user's comprehension of the material. Upon successfully completing the final assessment, participants will be awarded a certificate of completion and CPE credits representing their proficiency in the subject matter.

CPE credits can be applied toward industry certifications such as the Certified Payments and Fraud Prevention Professional (CPFPP) and the Certified Fraud Examiners (CFE).