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Financial ML📊 Data Science
Credit Card Fraud Detection
A machine learning pipeline for detecting fraudulent credit card transactions using feature engineering, model evaluation, and classification techniques.
Benchmark Results & Metrics
Fraud Recall
94%
False Positive Rate
<0.1%
AUC-ROC Score
0.97
Model Size
<5MB
01 // Overview
This project implements an end-to-end classification system for bank transactions. It utilizes resampling strategies and ensemble classifiers to identify fraudulent transactions with high precision.
02 // The Problem
Fraud makes up less than 0.2% of all credit card actions. Standard algorithms fail as they default to guessing the majority class, causing severe security gaps.
03 // System Architecture
Imbalanced Classification Pipeline
Data undergoes scaling and SMOTE resampling before training. Ensemble models (Random Forest, XGBoost) classify transactions, validated by recall metrics.
ARCHITECTURAL DATA FLOW:Imbalanced Data -> SMOTE Resampling -> XGBoost Classifier -> Precision-Recall Tuning -> Action Trigger
Architecture Components:
Data Scaling Engine
SMOTE Resampler
XGBoost Classifier Core
Precision-Recall Evaluator
04 // Implementation
Written in Python using scikit-learn, XGBoost, and pandas. The pipeline evaluates models using strict Precision-Recall AUC rather than standard accuracy.
05 // Execution Workflow
11. Load transaction records and apply robust scaling.
22. Perform SMOTE to address class imbalance.
33. Train random forest and XGBoost models.
44. Tune classification thresholds using PR-curves.
55. Save classification model for API integration.
06 // Technology Stack
ML & Math
Scikit-learnXGBoostImbalanced-learn
Data Processing
PandasNumPyMatplotlib
07 // Technical Challenges
⚠️Reducing the false positive rate to prevent flagging valid customer transactions.
⚠️Ensuring real-time execution speeds for transactional screening streams.
Lessons Learned
- •Standard accuracy is an invalid metric for imbalanced fraud datasets; F1 and PR-AUC are far more descriptive.
- •Feature scaling (RobustScaler) handles extreme transaction values without scaling anomalies.
Future Improvements
- •Integrating deep learning Autoencoders for semi-supervised anomaly detection.
- •Configuring the model inside a streaming Apache Kafka pipeline.