AI-Powered Financial Fraud Detection & Cybersecurity Analytics

Aim

Workshop Objectives

Workshop Structure

📅 Day 1: Financial Fraud Detection Using AI & Machine Learning

Core Objective: Develop a machine learning workflow for analyzing financial transactions, identifying suspicious activities, and evaluating fraud detection performance.

Topics Covered

• Introduction to financial fraud detection, transaction monitoring, and AI-based risk assessment.
• Understanding financial transaction datasets, fraud indicators, and class imbalance.
• Data preprocessing, feature engineering, and preparation of training and testing datasets.
• Supervised fraud classification using Logistic Regression and Random Forest.
• Introduction to Gradient Boosting and Isolation Forest for fraud and anomaly detection.
• Transaction risk scoring and interpretation of predicted fraud probabilities.
• Model evaluation using precision, recall, F1-score, confusion matrices, and ROC-AUC.
• Understanding false positives, false negatives, and fraud detection limitations.

🛠️ Hands-On Lab 1: Machine Learning-Based Financial Transaction Fraud Detection

• Import and explore a labelled financial transaction dataset.
• Preprocess transaction features and prepare training and testing datasets.
• Train Logistic Regression and Random Forest models for fraud classification.
• Evaluate model performance using confusion matrices, precision, recall, and F1-score.
• Visualize fraud predictions and identify high-risk transactions.

🧰 Tools Covered: Python, Google Colab, Pandas, Scikit-learn, Matplotlib.

Expected Outcome: A machine learning-based fraud detection model with transaction risk predictions and performance evaluation.

📅 Day 2: AI-Powered Phishing Detection & Digital Identity Fraud Analysis

Core Objective: Apply Natural Language Processing and machine learning to identify phishing messages while understanding digital identity verification and biometric fraud detection.

Topics Covered

• Understanding phishing attacks, social engineering, and digital identity fraud.
• Characteristics of phishing messages, suspicious URLs, and malicious communication patterns.
• Natural Language Processing for cybersecurity threat detection.
• Text preprocessing, tokenization, and TF-IDF feature extraction.
• Machine learning-based phishing classification using Logistic Regression.
• Digital identity verification and detection of document inconsistencies.
• Introduction to voice biometrics, synthetic voice fraud, and replay attacks.
• Phishing classification performance, false alerts, and detection limitations.

🛠️ Hands-On Lab 2: Phishing Email Classification Using NLP & Machine Learning

• Import and explore a labelled phishing and legitimate message dataset.
• Preprocess email text and extract relevant textual features.
• Apply TF-IDF vectorization for numerical feature representation.
• Train a Logistic Regression model for phishing classification.
• Evaluate predictions using precision, recall, F1-score, and a confusion matrix.
• Test sample messages and interpret phishing prediction probabilities.

🧰 Tools Covered: Python, Google Colab, Pandas, Scikit-learn, TF-IDF, Matplotlib.

Expected Outcome: An NLP-based phishing detection model with message classification results and performance metrics.

📅 Day 3: Advanced Fraud Detection, Model Optimization & Cyber Threat Intelligence

Core Objective: Understand advanced fraud detection strategies, decision threshold optimization, and cybersecurity threat intelligence through interactive experiments using the FraudShield Virtual Live Lab.

Topics Covered

• Advanced machine learning approaches for financial fraud detection.
• Fraud probability scores, risk-based classification, and decision threshold selection.
• Precision–recall trade-offs and cost-sensitive fraud detection.
• Comparative evaluation of supervised and anomaly detection models.
• Understanding fraud typologies, evolving attack patterns, and detection gaps.
• Digital identity verification and voice biometric fraud indicators.
• Fraud intelligence, suspicious transaction networks, and model monitoring.
• Improving fraud detection reliability through performance evaluation and stress testing.

🛠️ Hands-on: AI-Powered Fraud Detection & Cybersecurity Investigation

Hands-on Lab: Perform an integrated fraud detection experiment using the FraudShield Virtual Live Lab at Live-Labs.org.

• Analyze financial transactions and identify suspicious activities using AI-based risk scoring.
• Classify phishing messages and investigate digital identity and voice-based fraud indicators.
• Compare machine learning models and optimize fraud detection thresholds.
• Evaluate detection performance across simulated fraud attack patterns
• Analyze fraud intelligence, transaction networks, and model performance.

🧰 Tools Covered:

FraudShield Virtual Live Lab (Live-Labs.org), Transaction Scoring, Phishing Classifier, Identity Verification, Voice Biometrics, Model Workbench, Typology Stress Test, Fraud Intelligence.

Who Should Enrol?

Important Dates

Workshop Outcomes

Fee Structure