Programme Parameters
Advanced Postgrad
4 Weeks (40 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI for Fraud Detection: Basics is an accessible starting point for understanding how machine learning catches fraud. You learn the fundamental ideas — what makes fraud detectable, the difference between supervised classification and anomaly detection, and why fraud is a hard, imbalanced, adversarial problem — with clear examples rather than heavy maths. The course gives you the conceptual foundation and vocabulary to engage with fraud-detection systems. You finish with a solid grounding to build on toward applied fraud analytics. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This introductory course covers the basics of AI for fraud detection — core concepts, common techniques and how machine learning flags fraudulent activity.
📋 Course Objectives
1. Understand what makes fraud detectable.
2. Distinguish supervised and anomaly-based detection.
3. Grasp the challenge of imbalanced, adversarial data.
4. Follow the fraud-detection workflow.
5. Recognise where the approach fits.
👥 Who Should Enroll?
• Beginners in fraud and risk analytics
• Finance and operations professionals
• Students entering data analytics
• Anyone curious about fraud AI
🚀 Key Learning Outcomes
• A foundational grasp of fraud detection.
• The vocabulary to engage with the field.
• A springboard to applied analytics.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Introduction to AI in Fraud Detection
What is Fraud Detection? • Role of AI in Identifying Suspicious Activity • Traditional vs AI-Based Fraud Detection • Applications Across Finance, E-Commerce, and Insurance
Understanding Fraud Data
Types of Fraud Data and Transaction Data • Patterns, Behaviors, and Red Flags • Normal vs Suspicious Activity • Importance of Data Quality and Privacy
Basic AI Techniques for Fraud Detection
Introduction to Anomaly Detection • Risk Scoring Concepts • Pattern Recognition in Transactions • Simple Idea of Alerts and Fraud Flags
Applications and Responsible Use
AI in Banking Fraud Detection • AI in E-Commerce and Payment Security • Bias, Privacy, and False Alerts • Limitations of Fraud Detection Models
Future Scope and Learning Path
Emerging Trends in AI-Based Fraud Prevention • AI in Cybersecurity and Digital Trust • Career Opportunities in Fraud Analytics and Risk Management • Mini Learning Activity / Concept-Based Practice
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Artificial Intelligence |
| Covered Tool / Platform | Fraud Detection |
| Covered Tool / Platform | Anomaly Detection |
| Covered Tool / Platform | Transaction Monitoring |
| Covered Tool / Platform | Risk Scoring |
Frequently Asked Questions
Scholar Feedback & Reviews
Based on 0 scholar submissions
Rating Breakdown
No verified reviews published yet. Be the first to share your academic experience.
Leave Scholar Feedback
Your rating will help prospective scholars. Ratings below 3 stars are routed privately to the faculty mentor for immediate response.
