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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