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1. Title Slide
• Fraud Detection Insights for Online Payments
• Dataset: Credit-Card Fraud 2013-EUR (Kaggle)
• Presented by: Your Name
• Date: August 2025

2. 1. Introduction
• Understanding the importance of online
payment fraud detection
• Overview of supervised and unsupervised
approaches
• Need for real-time analysis and adaptive
systems

3. 2. Dataset Overview
• Source: Kaggle - European card transactions
(2013)
• Total Records: 284,807
• Fraudulent Transactions: 492 (~0.172%)
• Features: Time, Amount, V1-V28 (PCA
transformed), Class

4. 3. Problem Statement
• Goal: Detect fraud transactions from
anonymized data
• Challenges: Class imbalance, lack of
identifiable features
• Need for robust, scalable fraud detection
pipeline

5. 4. Data Preprocessing
• Check for null/missing values
• Normalize/scale features like Amount
• Time converted to hour of day
• Label encoding unnecessary due to numeric
features

6. 5. Exploratory Data Analysis
• Class imbalance visualization
• Transaction Amount Distribution
• Hourly transaction patterns
• Correlation heatmaps among principal
components

7. 6. Class Imbalance Challenges
• Fraud cases are less than 0.2%
• Standard accuracy metric is misleading
• Focus on Recall, Precision, F1-score, and ROC
AUC

8. 7. Techniques to Handle Imbalance
• SMOTE - Synthetic Minority Over-sampling
Technique
• Random undersampling
• Hybrid sampling
• Cost-sensitive learning (e.g., class weights)

9. 8. Modeling Approaches
• Baseline: Logistic Regression
• Tree-based: Decision Tree, Random Forest
• Boosted Models: XGBoost, LightGBM,
CatBoost
• Advanced: TabNet, AutoML frameworks

10. 9. Unsupervised & Deep Learning
Models
• Autoencoders for anomaly detection
• RBMs and GANs for fraud pattern generation
• LSTM networks for sequence detection
• Hybrid: DeepNet + KNN

11. 10. Feature Engineering
• Aggregate transaction statistics per card
• Rolling time-window features
• Transaction frequency features
• Graph-based features like centrality, clustering
coefficient

12. 11. Evaluation Metrics
• Confusion Matrix
• Precision = TP / (TP + FP)
• Recall = TP / (TP + FN)
• F1-Score =
2*(Precision*Recall)/(Precision+Recall)
• ROC AUC Curve

13. 12. Model Performance Summary
• XGBoost: AUROC ~0.989
• Random Forest: AUROC ~0.988
• Logistic Regression: Baseline
• Deep Learning + SMOTE: F1-score ~0.95

14. 13. Real-World Constraints
• Latency: real-time prediction under 1s
• Scalability: millions of transactions/hour
• Explainability for legal compliance
• Concept drift over time

15. 14. Concept Drift & Retraining
• Continuous learning is required
• Drift detection algorithms
• Model versioning and monitoring
• Auto-retraining pipelines

16. 15. Recommendations
• Use ensemble methods like LightGBM
• Balance classes using SMOTE
• Incorporate graph features
• Deploy using scalable APIs with monitoring

17. 16. Future Work
• Introduce federated learning for privacy
• Integrate behavioral biometrics
• Build interpretable AI models (SHAP/LIME)
• Combine with geolocation and device
fingerprints

18. 17. References
• •
https://www.kaggle.com/datasets/mlg-ulb/cre
ditcardfraud
• • SMOTE: SSRN 4412674
• • XGBoost & RF performance:
arXiv:1904.10604
• • Deep Learning models: arXiv:2205.15300
• • Concept Drift: arXiv:2010.06479