Modern Digital Fraud Data-Driven Detection & Prevention

[Emperor2011]

Modern Digital Fraud: Data-Driven Detection & Prevention
Published 4/2026
Created by Maya Fudim
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 38 Lectures ( 3h 1m ) | Size: 1.4 GB
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Hands-on Python Detection and Proactive Prevention for Today's Fraud Analysts and Product Managers.
What you'll learn
✓ Think like a fraudster: identify weak points through structured fraud journey analysis
✓ Understand the terminology of digital and cyber fraud landscape
✓ Build advanced detection logic using Python, including anomaly detection and clustering
✓ Train machine learning models to predict fraud using behavioral and device data
✓ Work with realistic datasets that reflect real platform challenges
✓ Design friction-right user flows that stop bots without hurting real users
✓ Participate in strategy and roadmap discussions with a strong technical foundation
✓ Get ready for technical interviews in fraud, risk, and data roles
Requirements
● No prior experience in fraud detection needed, you'll learn everything you need to know
Description
This course contains the use of artificial intelligence.
Digital fraud isn't what it used to be. Today's attackers use AI, automation, and large-scale bot networks to bypass traditional defenses in real time.
This course shows you how to fight back using data.
Modern Digital Fraud: Data-Driven Detection & Prevention is built for Fraud Analysts, Product Managers, and Data Scientists who want to design smarter, more resilient systems. You'll learn how modern attacks actually work-and how to detect and stop them using practical data science techniques.
Instead of theory, you'll work through real-world scenarios across eCommerce, fintech, gaming, and SaaS, using the same tools and approaches used in production environments.
What you'll learn
• Think like a fraudster: identify weak points through structured fraud journey analysis
• Build advanced detection logic using Python, including anomaly detection and clustering
• Train machine learning models to predict fraud using behavioral and device data
• Work with realistic datasets that reflect real platform challenges
• Design friction-right user flows that stop bots without hurting real users
• Participate in strategy and roadmap discussions with a strong technical foundation
• Get ready for technical interviews in fraud, risk, and data roles
Who this is for
• Fraud & Risk Analysts who want to apply a modern, deep understanding of digital fraud
• Product Managers building secure, growth-ready systems
• Data Scientists looking to apply their skills to fraud and cybersecurity
• Developers expanding into high-impact, real-world risk problems
• Executives in digital oriented companies
Fraud is evolving fast. Your defense needs to evolve faster.
Who this course is for
■ Data analysts, Data Scientists, Product Managers, Developers and Executives in digital oriented domains such as E-commerce ,SaaS and Fintech,



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