Financial institutions and digital platforms face growing challenges with fraudulent activities, such as unauthorized transactions, identity theft, and account takeovers. Traditional rule-based systems are slow to adapt and often lead to high false positives, frustrating genuine users and leaving gaps for sophisticated fraudsters. A predictive AI-driven system is required to detect fraud in real-time, prevent losses, and ensure customer trust.

• Build a real-time fraud detection system capable of analyzing transactions instantly.
• Reduce false positives while maximizing fraud detection accuracy.
• Leverage both vendor-provided external data (e.g., device intelligence, IP reputation) and internal historical fraud patterns.
• Continuously evaluate and improve fraud detection models to stay ahead of new fraud tactics.
• Provide actionable reports and dashboards for fraud prevention teams.

Data Ingestion:

• Collect real-time transaction streams and metadata.
• Use vendor data for new or unverified users (e.g., geolocation, IP risk score).
• Use internal data (historical fraud, transaction history, device patterns) for existing users.

Data Preprocessing:

• Clean and normalize transaction logs.
• Identify key data points (e.g., transaction amount, velocity, geolocation, device ID, merchant category).

Model Development:

• Train predictive models (Random Forest, LightGBM, XGBoost) to classify transactions as fraudulent or legitimate.
• Apply hyper parameter tuning to minimize false positives while improving fraud detection.

Validation & Evaluation:

• Validate models against historical fraud cases.
• Compare results across models using metrics like AUC, precision, recall, and F1-score.
• Select the model offering the best trade-off between fraud detection and customer experience.

Deployment & Monitoring:

• Deploy models on Databricks with streaming support for real-time inference.
• Generate alerts and risk scores instantly for suspicious transactions.
• Re-evaluate models periodically using new fraud patterns.

Data Collection & Integration

• Vendor data used for new/unverified transactions.
• Internal fraud detection history leveraged for supervised model training.
• Transactional and behavioral data stored in MySQL.

Data Cleaning & Feature Engineering

• Removed noise from transaction logs and standardized data formats.
• Engineered features such as transaction velocity, unusual geolocation, device fingerprint mismatches, and time-of-day anomalies.

Model Training & Validation

• Implemented Random Forest, LightGBM, and XGBoost models using PySpark on Databricks.
• Performed hyperparameter tuning to maximize fraud detection accuracy.
• Validated on past fraud cases to check generalizability.

Performance Evaluation

• Models compared using AUC, recall (fraud detection rate), precision (to reduce false alarms), and latency (real-time suitability).
• Chose the model providing the best balance between fraud prevention and customer experience.

Reporting & Continuous Monitoring

• Generated fraud detection reports highlighting transaction risk levels.
• Set up automated re-training and evaluation cycles to adapt to evolving fraud patterns.

• Data Storage: MySQL
• Data Processing: PySpark, Databricks
• Programming Language: Python
• Machine Learning Models: Random Forest, LightGBM, XGBoost
• Model Optimization: Hyperparameter Tuning
• Deployment & Monitoring: Databricks Streaming, Real-Time Dashboards