Project Description:

The AI Fraudulent Transaction Detection System is a machine learning-powered platform designed to automatically identify and flag suspicious or potentially fraudulent financial transactions in real-time. By analyzing historical transaction data and learning patterns of genuine vs. fraudulent activity, the system alerts users or administrators about anomalies before they escalate into financial loss.

This project is highly relevant in banking, e-commerce, fintech, and digital payment gateways.

Core Objective:

To detect fraudulent financial transactions using AI/ML models that analyze transaction behavior patterns and flag anomalies in real-time.

Key Features:

1. Transaction Data Analysis:

   • Monitor user behavior across transactions (location, amount, time, device, etc.).

   • Identify deviations from usual patterns.

2. Machine Learning Model:

   • Trained on labeled data (fraudulent vs. genuine).

   • Uses algorithms like Random Forest, Logistic Regression, or Neural Networks.

3. Fraud Scoring & Alerting:

   • Assigns a fraud probability score to each transaction.

   • Flags transactions that exceed a defined risk threshold.

4. User & Admin Dashboards:

   • Users get alerts on suspicious activity.

   • Admins review flagged transactions and take action.

5. Model Retraining Module:

   • Continual learning with feedback on false positives/negatives.

6. Behavioral Profiling:

   • Learns normal behavior per user over time (e.g., location patterns, spending habits).

Tech Stack:

 Backend:

• Node.js / PHP / Java (REST API for transaction submission and result retrieval)

• ML model deployed using Python (Flask/FastAPI) or via TensorFlow.js

 Frontend:

• HTML, CSS, Bootstrap, JavaScript

• Dashboard to show transaction status and fraud alerts

 Machine Learning:

• Python (Pandas, Scikit-learn, TensorFlow/Keras)

• Model: Logistic Regression, Random Forest, or Deep Neural Network

 Database:

• MySQL or MongoDB for storing transactions and flagged alerts