Digital Fraud Forensics: Transaction Anomaly Detection, Phishing Threat Analysis & Machine Learning
Uncover Hidden Fraud Patterns, Investigate Cyber Threats & Build Intelligent Detection Models.
Skills you will gain:
About Workshop:
This 3-day hands-on workshop explores how Artificial Intelligence (AI), Machine Learning (ML), and cybersecurity analytics are applied to detect financial fraud, identify phishing threats, and assess digital identity risks.
Participants will learn to analyze financial transaction datasets, develop fraud detection models, classify phishing messages using Natural Language Processing (NLP), and evaluate machine learning performance using precision, recall, F1-score, and confusion matrices.
The workshop combines Python-based computational exercises with practical cybersecurity investigations. On Day 3, participants will explore advanced fraud detection and model optimization using the FraudShield Virtual Live Lab at Live-Labs.org, including transaction risk scoring, phishing classification, identity verification, fraud typology testing, and threat intelligence analysis.
Aim: To equip participants with practical skills in machine learning-based financial fraud detection, phishing classification, transaction risk assessment, and cybersecurity analytics through computational workflows and interactive virtual laboratory experiments.
Workshop Objectives:
- Understand the application of AI and machine learning in financial fraud detection and cybersecurity.
- Analyze transaction datasets and identify patterns associated with fraudulent activities.
- Develop and evaluate machine learning models for fraud classification.
- Apply NLP and TF-IDF techniques for phishing message detection.
- Understand anomaly detection, fraud risk scoring, and decision threshold optimization.
- Explore digital identity verification and voice biometric fraud detection.
- Evaluate fraud detection performance using precision, recall, F1-score, and confusion matrices.
- Investigate fraud scenarios and detection weaknesses using the FraudShield Virtual Live Lab.
What you will learn?
📅 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.
Mentor Profile
Fee Plan
Important Dates
12 Nov 2026 Indian Standard Timing
12 Nov 2026 to 14 Nov 2026 Indian Standard Timing 7:30 PM
Get an e-Certificate of Participation!
Intended For :
- Students in AI, ML, Data Science, Computer Science and FinTech
- PhD scholars and researchers
- Faculty and academicians
- Banking and FinTech professionals
- Fraud and AML analysts
- Risk professionals
- Cybersecurity professionals
- Financial-crime investigators
- Data scientists
- Developers interested in financial AI
- Digital identity and transaction-monitoring professionals
Career Supporting Skills
Workshop Outcomes
By the end of this workshop, participants will be able to:
- Analyze financial transaction datasets and identify suspicious activity patterns.
- Develop machine learning models for fraud classification using Python and Scikit-learn.
- Apply NLP and TF-IDF techniques for phishing email detection.
- Evaluate fraud detection models using precision, recall, F1-score, and confusion matrices.
- Interpret fraud risk scores and optimize classification decision thresholds.
- Understand digital identity verification and voice biometric fraud detection.
- Investigate fraud typologies and identify machine learning model limitations.
- Perform integrated fraud detection and cybersecurity experiments using the FraudShield Virtual Live Lab.