Fraud Guard

Inspiration

The increasing complexity of digital transactions has brought a surge in financial fraud. With minority communities being disproportionately affected by fraud, I wanted to build a system that could help safeguard their financial well-being. Inspired by the idea of making real-time fraud detection accessible and effective, I set out to create FraudGuard, a system that uses transaction patterns to flag unusual behaviors and alert users and institutions.

What it does

FraudGuard analyzes user transaction patterns, detects unusual activity by comparing recent transactions to historical averages, and flags potential fraud.

How we built it

We built FraudGuard using Python for data processing and SQL for storing and querying user transaction data, combined with a simple algorithm to detect abnormal transactions based on predefined thresholds. We also used Streamlit, API, and Open AI in assisting with the presentation of the data.

Challenges we ran into

We faced challenges in managing large datasets efficiently, calibrating fraud detection thresholds, and integrating real-time data processing between SQL and Python.

Accomplishments that we're proud of

Successfully creating an automated system that dynamically detects and flags fraudulent transactions in real time was a major accomplishment.

What we learned

We learned how to efficiently preprocess transaction data, integrate SQL with Python, and develop a fraud detection algorithm based on transaction history.

What's next for Fraud Guard

Next steps include refining the fraud detection algorithm, adding more advanced features like machine learning, and scaling the system for wider use across different financial platforms.