Financial Fraud Detection & Risk Mitigation System

Project Overview

This project implements a comprehensive fraud detection pipeline leveraging advanced data preprocessing, feature engineering, and ensemble modeling to identify suspicious activity in transactional data with high accuracy and recall.

Objectives

• Detect fraudulent transactions rapidly and reliably across millions of data points
• Maximize recall to minimize undetected fraud while maintaining strong precision
• Build scalable models robust to class imbalance and evolving fraud patterns

Data & Feature Engineering

• Utilizes detailed transactional data including anonymized numerical attributes and temporal patterns
• Derives advanced features capturing temporal behavior, transaction velocity, and aggregate statistics
• Transforms cyclical time variables (e.g., hour-of-day) to better encode periodic patterns
• Applies robust scaling to improve convergence and handle skewed distributions

Modeling Architecture

The system integrates multiple specialized classifiers into a stacked ensemble architecture designed for high-performance fraud detection under severe class imbalance. Key components include:

• Dimensionality Compression: Captures latent structure via unsupervised reconstruction techniques
• Time-Aware Feature Blocks: Encodes short- and long-term transactional behavior
• Gradient-Ensembled Classification: Combines tree-based learners with rare-event reweighting
• Probability Calibration: Post-model calibration improves interpretability of fraud scores

Performance & Evaluation

The model was evaluated on a stratified test set using key classification metrics. Below are the results:

Confusion Matrix:
[[56846    18]
 [   16    82]]

Classification Report:
              precision    recall  f1-score   support

           0     0.9997    0.9997    0.9997     56864
           1     0.8200    0.8367    0.8283        98

    accuracy                         0.9994     56962
   macro avg     0.9099    0.9182    0.9140     56962
weighted avg     0.9994    0.9994    0.9994     56962
  

These results reflect over 82% precision and 83.7% recall on the fraud class, with an overall accuracy of 99.94%. The model achieves strong identification power while maintaining a very low false positive rate — critical for real-world deployment in financial systems.

Business Impact

This fraud detection system is designed for high-throughput financial environments, offering real-time inference capability and modular integration into existing fraud/risk infrastructure. It supports:

• Real-time monitoring of transactional pipelines
• Batch risk scoring for auditing and compliance
• Scalable alert generation and risk reporting workflows

Code

If you want access to the codebase, reach out to schilamkur@gmail.com.