Payment Fraud Detection System

[VisibleNasir]

📘 Overview

Implement a fraud detection system to identify suspicious or fraudulent payment transactions in real-time.
This will help reduce losses, protect users, and maintain trust in the platform.

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💡 Why Add It

• Security: Detect unauthorized or suspicious transactions.
• Compliance: Reduce risk of regulatory penalties.
• Revenue Protection: Minimize losses due to fraudulent activity.

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⚙️ Implementation

🔹 Model

• Binary classification: Fraud (1) vs Legitimate (0)
• Inputs (features):
  • Transaction amount
  • Payment method (UPI, card, wallet)
  • Timestamp / transaction hour
  • Device / IP info
  • Merchant ID
  • User history (avg transaction, frequency, velocity)
• Algorithms:
  • Logistic Regression / Random Forest / XGBoost
  • Optionally, anomaly detection models for unsupervised learning

🔹 Data Pipeline

1. Collect transactions in database (@repo/db).
2. Preprocess: normalize amounts, encode categorical features, extract time-based features.
3. Split dataset: train / test / validation.
4. Handle class imbalance: use SMOTE or weighted loss.

🔹 Webhook / API Integration

• Endpoint: /app/api/fraud/check
• Input: transaction payload
• Output: { isFraud: true/false, score: 0-1 }
• Optional: block or flag suspicious transactions before processing payment

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🗄️ Database (Prisma)

• Add isFraud and fraudScore fields to Transaction table: