Fraud Detection with AI: Building Intelligent Systems to Combat Financial Crime
artificial-intelligence-ai.
Course Modules:
Module 1: Understanding Fraud in Financial Systems
Common types of fraud: credit card, transaction, identity theft, insurance
Characteristics of fraud: rarity, evolution, deception
Business and legal impacts of fraud
Module 2:preparing Data for Fraud Detection
Collecting and labeling fraud data
Handling imbalanced datasets with resampling techniques
Feature engineering from transaction logs and user behavior
Module 3: Supervised Learning for Fraud Classification
Logistic regression, decision trees, and ensemble models
XGBoost and LightGBM for high-performance classification
Evaluation metrics: precision, recall, F1-score, ROC-AUC
Module 4: Anomaly Detection and Unsupervised Techniques
Isolation Forests, Autoencoders, One-Class SVM
Detecting novel or emerging fraud patterns
Unsupervised pre-training for low-label environments
Module 5: Real-Time Fraud Prevention Pipelines
Building scalable pipelines with Python and stream processing
Risk scoring models and alert systems
Model explainability with SHAP and LIME
Module 6: Capstone Project – Build an AI-Powered Fraud Detection System
Choose a use case (e.g., credit card fraud, loan fraud, account takeover)
Train and evaluate a detection model
Submit your pipeline, model dashboard, and analysis report
Tools & Technologies Used:
Python
Scikit-learn, XGBoost, LightGBM
Imbalanced-learn for oversampling/undersampling
SHAP, LIME, and matplotlib for interpretability
Target Audience:
Financial analysts and fraud investigators
Machine learning engineers in fintech and cybersecurity
Students pursuing careers in AI and risk analytics
Developers building AI-based security systems
Global Learning Benefits:
Learn how to detect fraud with real-world AI methods
Handle imbalanced and deceptive datasets effectively
Design scalable, explainable systems that adapt to new fraud patterns
Support safe, trusted digital finance through intelligent automation
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