Project Gamma was a high-stakes data analysis project for FinServe Bank. The objective was to uncover hidden patterns in transaction data to detect potential fraud and optimize loan approval workflows.
The Challenge
The bank had terabytes of unstructured data. Manual analysis was impossible, and existing automated rules were generating too many false positives.
Our Solution
We implemented a machine learning pipeline using Python and TensorFlow. Our models were trained on historical data to identify complex fraud patterns with high precision.
Technologies Used
- Data Science: Python, Pandas, Scikit-learn
- ML Framework: TensorFlow
- Visualization: Tableau, D3.js
Key Results
Detected $500k worth of fraudulent transactions
Reduced false positives by 35%
Automated 80% of the loan pre-approval process
Project Info
Client
FinServe Bank
Duration
6 Months