Machine Learning For Credit Card Fraud Detection

Machine Learning For Credit Card Fraud Detection

Financial Security · Fraud Analytics

Python · Feature Engineering · Cross-Validation · Model Serving

Operational Focus: sub-second fraud classification on real-time banking transactions under extreme class imbalance.

Project Abstract & Technical Scope

This project introduces an end-to-end, production-ready machine learning framework for Machine Learning For Credit Card Fraud Detection. Within real-world operational environments, systems face severe obstacles including severe class imbalance (fraud < 0.1%), evolving adversary tactics, and strict 50ms authorization SLAs. 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 terminal POS authorizations, cardholder velocity counters, online payment gateway streams, and geolocation shifts. 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: XGBoost with Focal Loss, CatBoost Classifier, Isolation Forest, Graph Neural Network Embeddings. 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 (Average Precision), False Alarm Rate (FAR), Recall at 99% Precision, Financial Loss Prevented ($) 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

• • XGBoost with Focal Loss
• • CatBoost Classifier
• • Isolation Forest
• • Graph Neural Network Embeddings

Final production selection is based on cross-validated Pareto efficiency balancing PR-AUC (Average Precision), False Alarm Rate (FAR), Recall at 99% Precision, Financial Loss Prevented ($) against inference latency.