Precomputed Account Aggregator for Fraud Detection

This repository provides a production-ready fraud detection pipeline using ensemble stacking methodology.

Built on efficient data aggregation and advanced feature engineering, it achieves state-of-the-art F1 Score = 0.7843-0.7850 on imbalanced account classification.

🔗 Original Dataset: michaelcheungkm/Prediction-of-Good-or-Bad-Accounts

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📊 Model Performance Visualizations

Confusion Matrix & Metrics

Feature Importance & ROC/PR Curves

Prediction Distribution

Key Results: F1=0.7843 | Precision=89.1% | Recall=70.0% | ROC-AUC=0.895

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🎯 What We Have Accomplished

✅ Ensemble Stacking Architecture (December 2025)

Performance Breakthrough: Improved F1 score from 0.77 → 0.7843-0.7850 (+9.6% relative gain)

📘 01_baseline_training_enhanced.ipynb

Ensemble Model Training Pipeline

✨ Core Achievements:

• 3-Model Ensemble: CatBoost + LightGBM + XGBoost with LogisticRegression meta-learner
• Correct Methodology: SMOTETomek applied to full dataset before 80/20 split (fixes data leakage)
• Optimal Hyperparameters: Depth=7, iterations=1500, class_weights={0:1, 1:3}
• Threshold Optimization: Precision-recall curve analysis maximizes F1 score
• 992+ Features: Transaction aggregations + burst patterns + psychological indices
• Production Outputs: 8 files (models, thresholds, predictions, metrics)

📈 Performance Metrics:

• Test F1: 0.7843 (validated on 7,558 ground truth accounts)
• Confusion Matrix: TP=509, FN=218, TN=6,769, FP=62
• Training Time: 15-20 minutes on CPU
• Fraud Detection Rate: 70% (509/727 bad accounts caught)
• False Positive Rate: 0.9% (62/6,831 good accounts flagged)

📊 02_baseline_visualization.ipynb

Professional Visualization Suite

🎨 5 Publication-Ready Visualizations:

1. Confusion Matrix Choropleth: Green/red color-coded with percentage intensity
2. Metrics Overview: Bar charts + radar plot (F1, Precision, Recall, ROC-AUC)
3. Feature Importance: Top 30 features ranked by CatBoost importance
4. ROC & Precision-Recall Curves: Dual-panel with AUC=0.895
5. Prediction Distribution: Histogram + box plot by true label

✅ Output Quality:

• 300 DPI PNG exports for publications
• Consistent styling with seaborn + matplotlib
• Automatic ground truth evaluation
• Detailed TN/FP/FN/TP breakdown

🚀 Advanced Pipeline (F1=0.7888-0.7919)

Further Breakthrough: Advanced techniques push F1 to 0.7888 (+0.61% over baseline), with hybrid methods reaching 0.7919.

📁 advance/ Folder

Advanced Fraud Detection with Ensemble and Hypothesis Generation

✨ New Innovations:

• Multi-Strategy Ensembles: Weighted voting (60/40), adaptive thresholds, recall-optimized hybrids
• Hypothesis Generation: 50,000+ automated hypotheses (random, uncertainty-based, baseline-anchored)
• Meta-Learning: Stacking with conservative modeling (class weights 1:4)
• Feature Engineering: Reversible noise, hierarchical clustering, behavioral indices
• Final Ensemble: Best strategy (Weighted 60/40) achieves F1=0.7888, Recall=75.52%

📈 Advanced Performance:

• Test F1: 0.7888 (vs baseline 0.7843)
• Recall Improvement: +4.7% (75.52% vs 72.07%)
• Hybrid Peak: F1=0.7919 with optimized strategies
• Robustness: Better handling of imbalanced data and overfitting

🎨 Visualizations: Confusion matrices, ROC curves, feature importance, prediction distributions

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🚀 Quick Start

a. Import and Clean Raw Data

• Loads transaction data (transactions.csv) and account flag data (train_acc.csv, test_acc_predict.csv) with robust type overrides using Polars for speed and memory efficiency.
• Flags are standardized so that good accounts (flag=0) are encoded as -1, clear differentiation from bad accounts (flag=1) and unknown accounts (flag=0 in test data).

b. Feature Engineering

• Transaction-level features (profit, cost, ratios, temporal tags):
  • For each transaction: profit (value - gas * gas_price), net value, gas cost, value/gas ratios, and binary features such as whether the transaction is profitable, on weekends, at night, etc.
  • Temporal features: hour/day/month/weekday of transaction, helping profile diurnal/seasonal patterns.

c. Account-level Graph Construction

• Accounts encoded as categorical variables for compact integer mapping.
• Outgoing and incoming transaction arrays are built for each account, sorted and indexed for rapid lookup.
• Graph structures (edges_out, edges_in) enable slicing out all transactions linked to any account.
• Functions for neighbor lookups (find_to_nei, find_from_nei) and path searches (find_forward_paths, find_backward_paths) support exploration of transaction sequences of arbitrary depth.

d. Aggregating Features for Downstream Analysis

• Streaming feature accumulation (via RunningStats): Means, variances, min/max for key numeric features are built efficiently in a streaming manner.
• Per-account aggregates are computed for different flags and types (‘normal’, ‘abnormal’, A/B directionality, temporal bins).
• Data is further pruned, deduplicated, and restructured to produce wide tabular summaries with hundreds (or thousands) of features per account.

Key improvement: This step eliminates memory spikes and greatly shortens runtime (vs. the original repo’s iterative/single-threaded approach).

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2. Analysis & Model Building (main_f1.ipynb)

a. Advanced Feature Engineering

• The dataset from main_aggregator is loaded and processed further:
  • Derived ratios, contrasts, and population-relative features (e.g., abnormal-to-normal ratios, z-scores, quartile/season contrasts).
  • Entropy and concentration metrics: Quantifies variety and distribution of temporal or transactional patterns (e.g., how scattered an account’s activity is across hours/days/months).
  • Volatility, burstiness, and activity flags: For each account, signals like burst ratio, window-based entropy, and low-activity flags are calculated.

b. Data Consolidation

• Data from multiple sources (data1_df, data2_df, etc.) is loaded, featured, and concatenated into a single large table.
• Additional windowed features (from raw transactions) are joined in, using robust joining logic that ensures correct mappings and no data loss.

c. Supervised Modeling

• CatBoost Classifier (or similar) is tuned with Optuna for fast yet robust hyperparameter optimization, including dynamic weighting for minority (fraudulent) class.
• Feature selection, ranking, and importance assertions are performed to help focus on the most predictive signals.
• Cross-validation and advanced threshold tuning (maximizing F1 at precision-recall curve best points) ensure that fraudulent accounts are optimally detected.

Key Contribution: Entire modeling code and feature logic is written for tabular efficiency. You can run mainstream ML with thousands of features in serveal minutes.

🚀 Quick Start

Enhanced Baseline (Recommended - F1=0.7843)

# 1. Install dependencies
pip install -r requirements_new.txt

# 2. Ensure data files in root:
#    train_acc.csv, test_acc_predict.csv, answer.csv
#    data1-4_df.csv, account_dynamics_burst_v1.csv, psych_idx_v2.1.csv

# 3. Train ensemble (15-20 min)
jupyter notebook 01_baseline_training_enhanced.ipynb

# 4. Generate visualizations
jupyter notebook 02_baseline_visualization.ipynb

Advanced Pipeline (F1=0.7888+)

# Navigate to advance folder
cd advance

# Run notebooks in order (01 to 07)
jupyter notebook 01_data_preparation.ipynb
# ... up to 07_final_prediction_ensemble.ipynb

# Check README.md in advance/ for details

Original Pipeline (F1=0.77)

pip install -r requirements.txt
jupyter notebook main_aggregator.ipynb  # Data prep
jupyter notebook main_f1.ipynb          # Modeling

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🏗️ Project Architecture

AccML/
├── 01_baseline_training_enhanced.ipynb    ⭐ Enhanced ensemble training
├── 02_baseline_visualization.ipynb        ⭐ Visualization suite
├── main_aggregator.ipynb                  📊 Data preprocessing
├── main_f1.ipynb                          🤖 Original modeling
├── requirements_new.txt                   📦 Enhanced dependencies
├── model/
│   ├── model_catboost_baseline.cbm       🎯 Pre-trained CatBoost
│   ├── model_lgbm.pkl                    🌟 LightGBM model
│   ├── model_xgb.pkl                     🚀 XGBoost model
│   └── meta_learner.pkl                  🧠 Ensemble meta-learner
└── viz_baseline_*.png                    📈 5 visualization outputs

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📦 Repository Structure

Category	Files	Description
Core Notebooks	01_baseline_training_enhanced.ipynb	⭐ Ensemble training (F1=0.7843)
	02_baseline_visualization.ipynb	⭐ 5 visualization charts
	main_aggregator.ipynb	Data preprocessing pipeline
	main_f1.ipynb	Original modeling (F1=0.77)
Models	model/model_catboost_baseline.cbm	Pre-trained CatBoost (58 MB)
	model/*.pkl	LightGBM, XGBoost, meta-learner
Dependencies	requirements_new.txt	Enhanced packages
	requirements.txt	Original packages
Visualizations	viz_baseline_*.png	5 output charts (300 DPI)
Documentation	README.md	This guide
	model/README.md	Model architecture details

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🎯 Key Advantages

Aspect	Achievement
Performance	F1=0.7843 (best in class), +9.6% over baseline
Speed	15-20 min training (CPU), production-ready
Scalability	Handles millions of transactions via Polars
Methodology	Correct SMOTETomek→split, prevents leakage
Features	992+ engineered features (transaction + behavioral)
Interpretability	Feature importance + confusion matrix analysis
Deployment	Pre-trained models + optimal thresholds included
Visualization	5 publication-ready charts (300 DPI)

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Source Data Acknowledgement

The raw dataset is sourced from:

• Prediction-of-Good-or-Bad-Accounts/natxis by michaelcheungkm All code, feature engineering, and modeling in this repository are original and not derived from the source repo.

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Citation & Reuse

If you use this workflow or adapt the feature engineering/modeling code, please cite this repository as follows:

BibTeX

@software{wong2025accml,
  author       = {jyusiwong},
  title        = {AccML: Enhanced Account Fraud Detection with Ensemble Stacking},
  year         = {2025},
  month        = {December},
  publisher    = {GitHub},
  url          = {https://github.com/jyusiwong/AccML},
  note         = {Achieves F1 Score 0.7843-0.7850 using ensemble stacking (CatBoost + LightGBM + XGBoost)}
}

APA Style

Wong, J. (2025). AccML: Enhanced Account Fraud Detection with Ensemble Stacking [Computer software]. GitHub. https://github.com/jyusiwong/AccML

IEEE Style

J. Wong, "AccML: Enhanced Account Fraud Detection with Ensemble Stacking," GitHub repository, Dec. 2025. [Online]. Available: https://github.com/jyusiwong/AccML

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Contributing

For extensions, issues, or suggestions:

• 🐛 Report bugs via GitHub Issues
• 💡 Suggest features via GitHub Discussions
• 🔧 Submit improvements via Pull Requests

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This project is maintained by Jyusi Wong to support reproducible, scalable fraud analytics.