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  Home Page

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  Batch Fraud Detection Dashboard for Companies

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  Mock Store Front

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  SMTP Email Notification

About FadeFraud

Inspiration

Financial fraud is a growing problem, costing businesses and consumers billions of dollars every year. Many fraud detection systems rely on static rule-based approaches, which struggle to adapt to evolving fraud techniques. We wanted to build a real-time fraud detection system that combines machine learning with rule-based heuristics to provide accurate, fast, and automated fraud alerts.

What It Does

FadeFraud is a machine learning-powered fraud detection system with two key use cases:

1. Batch Fraud Detection for Companies:

   • Businesses can upload a CSV file containing transaction data.
     
   • Our ML model processes the file and identifies fraudulent transactions, helping companies detect fraud patterns in bulk.
     

2. Real-Time Consumer Fraud Alerts:

   • When a consumer’s credit card is used, the transaction is automatically analyzed using the Plaid API.
     
   • The transaction data is passed through our ML model, which determines if it's fraudulent.
     
   • If fraud is detected, the user receives an email notification via SMTP, allowing them to take immediate action.
     

How We Built It

We developed FadeFraud using:

• Machine Learning Model: Trained on historical fraud data using scikit-learn and TensorFlow, incorporating rule-based heuristics and anomaly detection.
  
• Backend: Flask for handling API requests, Plaid for real-time credit card transaction tracking, and ngrok to deploy the backend to use as a webhook for Plaid.
• Frontend: React, Vite, MaterialUI, and Tailwind CSS for an intuitive, easy-to-use interface.
  
• Database: MongoDB to store transaction data and fraud alerts.
  
• Plaid API: Fetches real-time transaction data for fraud analysis.
  
• SMTP Email Notifications: Sends alerts to users when fraudulent transactions are detected.
  

Challenges We Ran Into

• Ensuring high accuracy: Fraud detection models must balance precision and recall to minimize false positives and false negatives.
  
• Real-time processing: Optimizing the latency of fraud detection when processing live transactions.
  
• Handling imbalanced data: Fraud cases are rare compared to legitimate transactions, so we used SMOTE (Synthetic Minority Over-sampling Technique) to improve model training.
  
• Integrating Plaid API: Managing API authentication and ensuring seamless transaction retrieval.
  

Accomplishments That We're Proud Of

• Successfully built a real-time fraud detection system that integrates machine learning and Plaid API.
  
• Designed an automated fraud alert system that notifies users instantly via email.
  
• Developed a dual-use platform that benefits both businesses and consumers.
  
• Improved fraud detection accuracy by fine-tuning rule-based heuristics and ML models.
  

What We Learned

• How to integrate real-time financial data APIs (Plaid) with machine learning.
  
• Advanced fraud detection techniques, including anomaly detection and hybrid ML-rule-based systems.
  
• Optimizing full-stack application deployment for fraud detection.
  
• The importance of real-time notifications and automated security alerts.
  

What's Next for FadeFraud

• Expanding the model with deep learning techniques for improved fraud detection accuracy.
  
• Implementing a mobile app for real-time fraud alerts.
  
• Partnering with banks and fintech companies to enhance fraud prevention.
  
• Adding explainability features, so users can understand why a transaction was flagged as fraudulent.
  

Built With

• flask
• materialui
• mongodb
• plaid
• python
• react
• scikit
• smtp
• tailwind
• tensorflow
• vite