Machine Learning For Fraud Detection E-Commerce

Machine Learning For Fraud Detection E-Commerce

E-Commerce Security · Trust & Safety

Python · Feature Engineering · Cross-Validation · Model Serving

Operational Focus: card-not-present (CNP) and promo-abuse fraud detection across digital checkout transactions.

Project Abstract & Technical Scope

This project introduces an end-to-end, production-ready machine learning framework for Machine Learning For Fraud Detection E-Commerce. Within real-world operational environments, systems face severe obstacles including device spoofing, friendly fraud chargebacks, and checkout friction minimization. The objective of this work is to implement a robust, leak-free computational pipeline that translates raw inputs into deterministic, high-confidence decision metrics.

The system ingests and processes records sourced from digital device fingerprints, IP proxy/TOR indicators, billing vs shipping address discrepancies, and checkout velocity. Raw attributes undergo automated data sanitization, multivariate imputation, distribution rebalancing, and outlier filtering. Continuous numerical features are scaled using robust statistical scaling techniques, while categorical, spatial, and temporal attributes receive cyclical encoding, high-cardinality target transforms, or dense embeddings.

Modeling evaluates diverse algorithmic paradigms: LightGBM Classifier, Random Forest, Isolation Forest, Deep Tabular ResNet. Rigorous validation protocols employ stratified, temporal, or grouped cross-validation to prevent train-test contamination. Hyperparameter optimization is systematically executed via Bayesian search strategies (Optuna), targeting optimization of PR-AUC, Chargeback Ratio Reduction, Precision at High Recall, Cost Savings ROI rather than uninformative global accuracy.

To ensure practical viability and regulatory transparency, global and local feature contributions are derived using TreeSHAP and Partial Dependence profiles. The winning configuration is serialized into portable ONNX format and served via an asynchronous FastAPI microservice equipped with telemetry logging for real-time concept drift detection.

Tools & Technologies

The standard modern data science stack utilized for feature extraction, model tuning, and REST deployment:

Modular Machine Learning Workflow

Candidate Algorithms Benchmarked

• • LightGBM Classifier
• • Random Forest
• • Isolation Forest
• • Deep Tabular ResNet

Final production selection is based on cross-validated Pareto efficiency balancing PR-AUC, Chargeback Ratio Reduction, Precision at High Recall, Cost Savings ROI against inference latency.