Banking Transaction Anomaly Detection System

Overview

ML-powered system for detecting anomalies in banking transactions using passive traffic data analysis. Deploy pre-trained models or integrate custom models to identify suspicious transaction patterns in real-time.

Key Features

• Real-time transaction anomaly detection
• Pre-trained ML models for common fraud patterns
• Customizable classification schemas
• Enterprise-grade security and compliance
• Scalable architecture for high-volume transactions

Quick Setup

1. Create Virtual Environment

python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt

2. Database Configuration

Copy .env.example to .env and configure your database:

cp .env.example .env
# Edit .env with your database credentials

3. Network Definition

Adjust network configuration based on your firewall and enterprise requirements:

• Configure traffic monitoring points
• Set up secure data ingestion endpoints
• Define network segmentation rules

4. Classification Schema

Define your anomaly classification categories:

• Fraud risk levels (Low, Medium, High, Critical)
• Transaction types to monitor
• Custom business rules and thresholds

5. Results Storage

Configure result storage and alerting:

• Set up database schemas for anomaly logs
• Configure real-time alerting systems
• Define compliance reporting formats

Model Integration

• Deploy pre-trained models for immediate use
• Add custom models trained on your transaction data
• Support for popular ML frameworks (TensorFlow, PyTorch, scikit-learn)

Production Deployment

Enterprise-ready with scalable architecture, audit trails, and compliance features for banking environments.

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This system provides advanced ML capabilities for financial institutions to detect and prevent fraudulent transactions through intelligent traffic analysis.