FRAUD ANALYTICS WITH AI/ML

Kick things off with a deep dive into what this course is all about. Meet your instructor, get the scoop on what’s ahead, and fire off any burning questions.

• Instructor introduction
• Course objectives & flow
• Q&A

Fraud isn’t just a bad plot twist — it’s a real, evolving challenge. Get a crash course in fraud analytics, how it differs from cybersecurity, and why AI/ML is the game-changer in catching fraudsters. Learn the key fraud types, how they operate, and how data-driven detection keeps organizations a step ahead. 

• Introduction to fraud analytics vs. cybersecurity
• Overview of the fraud landscape
• Key fraud types & associated methodologies
• AI/ML-driven fraud detection

Post-Class Quiz #1

Get comfortable with your coding setup and start working with Python for fraud analytics. You'll explore real and synthetic datasets, pick up essential data analysis techniques, and start spotting fraud patterns — all while building confidence for the road ahead.

• Environment setup
• Python overview & Jupyter Notebook
• Introduction to fraud data analysis
• Demonstration: EDA with synthetic data
• Workshop: Coding assignment: EDA with real data
• Discussion and Q&A

Assignment #1: Perform exploratory data analysis (EDA) on a provided real dataset. Document key findings, patterns, and potential fraud indicators in a Jupyter Notebook. Submit your completed notebook.

In this session, you'll break down how fraudsters operate, the different types of identity fraud, and why catching them isn’t as easy as it seems. Get ready to dissect the data, spot the red flags, and gear up for AI-driven solutions next class.

• What is identity fraud & how is it enacted?
• Business & customer impact of identity fraud
• How to detect identity fraud
• Case Study: Real-world identity fraud incident
• Discussion and Q&A

Post-Class Quiz #3

Spot fake identities before they cause real damage. In this class, you'll explore how AI and machine learning can catch identity fraud, build and test your own fraud detection model, and get hands-on with data preprocessing, feature engineering, and model training.

• AI/ML pipeline for identity fraud detection
• Detailed explanation of algorithms
• Demonstration with synthetic data
• Workshop: Coding assignment: Detecting identity fraud 
• Discussion and Q&A

Assignment #3: Create a classification model to detect fraudulent identities using an open-source dataset. Document the steps, from preprocessing to evaluation. Submit your Jupyter Notebook with a brief summary.

Understand how ATO & check kiting fraud work, their real-world impact, and the key signs to spot them. By the end, you'll have a solid grasp on these fraud types, ready to tackle AI/ML detection techniques in Class 8.

• Introduction to account takeover fraud (ATO)
• Introduction to check kiting fraud
• Business & operational impacts
• Detection challenges & indicators
• Discussion and Q&A

Post-Class Quiz #4

Get hands-on with AI/ML techniques to spot ATO and check kiting fraud. You’ll dive into fraud data, clean it up, and build models using both supervised and unsupervised learning. Gain real practice with model development and evaluation.

• Overview of AI/ML techniques for ATO & check kiting fraud
• Demonstration with synthetic data
• Workshop: Coding assignment
• Discussion and Q&A

Assignment #4: Train and evaluate AI/ML models for ATO and check kiting fraud detection using a provided dataset. Submit a Jupyter Notebook with your process, code, and results.

Uncover how synthetic identity fraud and deep fakes are made, why they’re a threat, and what businesses and individuals need to watch out for. Explore real examples, spot red flags, and lay the groundwork for using AI/ML to fight back.

• Introduction to synthetic identity fraud
• Introduction to deep fakes & AI voice cloning
• Business & operational impacts
• Detection challenges & indicators
• Case Study: Real-world scenarios of synthetic identities & deep fakes
• Discussion, Q&A

Post-Class Quiz #5

Get hands-on with advanced algorithms, like neural networks and anomaly detection, while learning how to process data and build fraud-detecting models. By the end, you'll be ready to handle real datasets and see how these tools are shaping fraud prevention.

• AI/ML pipeline for synthetic identity fraud and deep fakes
• Detailed explanation of algorithms
• Demonstration with synthetic data
• Workshop: Coding assignment
• Discussion and Q&A

Assignment #5: Develop AI/ML models for synthetic identity and deepfake detection using provided datasets. Submit a Jupyter Notebook with your process, results, and visualizations.

We’ll explore how financial institutions are staying one step ahead of fraudsters, and how AI is changing the game in compliance and detection. Get the know-how to navigate the evolving landscape of financial crime and regulatory demands.

• Introduction to KYC & AML
• AI/ML for KYC & AML compliance
• Emerging fraud trends & cyber threats
• Demo: Example Code Project: KYC/AML & End-to-End Fraud Detection System
• Discussion and Q&A

Post-Class Quiz #6

The final class gives you the lowdown on how to break into the field, the key skills you need, and the certifications that can set you apart. Plus, we’ll explore career paths in fraud detection, AI/ML, and beyond, so you can start building your roadmap to success.

• Career opportunities in fraud detection
• Skills & certifications to focus on
• How to get a job 
• Where to apply
• General career advice
• Closing discussion and Q&A